Pretrained Embedding¶

Setup¶

In [ ]:

pip install ydf tensorflow_hub tensorflow_datasets -U

pip install ydf tensorflow_hub tensorflow_datasets -U

In [1]:

import ydf  # To train the model
import tensorflow_datasets  # To download the movie review dataset
import tensorflow_hub  # To download the pre-trained embedding

import ydf # To train the model import tensorflow_datasets # To download the movie review dataset import tensorflow_hub # To download the pre-trained embedding

What is a pre-trained embedding?¶

Pretrained embeddings are models trained on a large corpus of data that can be used to improve the quality of your model when you do not have a lot of training data. Unlike a model that is trained for a specific task and outputs predictions for that task, a pretrained embedding model outputs "embeddings," which are fixed-size numerical vectors that can be used as input features for a second model (e.g. a ydf model) to solve a variety of tasks. Pre-trained embeddings are also useful for applying a model to complex or unstructured data. For example, with an image, text, audio, or video pre-trained embedding, you can apply a YDF model to image, text, audio, and video data, respectively.

In this notebook, we will classify movie reviews as either "positive" or "negative". For instance, the review beginning with "This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas ..." is a positive review. Our dataset contains 25000 reviews, but because 25000 reviews are NOT enough to train a good text model, and because configuring a text model is complicated, we will simply use the Universal Sentence Encoder pre-trained embedding.

Downloading dataset¶

We download the dataset from the TensorFlow Dataset repository.

In [2]:

raw_train_ds = tensorflow_datasets.load(name="imdb_reviews", split="train")
raw_test_ds = tensorflow_datasets.load(name="imdb_reviews", split="test")

raw_train_ds = tensorflow_datasets.load(name="imdb_reviews", split="train") raw_test_ds = tensorflow_datasets.load(name="imdb_reviews", split="test")

Let's look at the first 200 letters or the first 3 examples:

In [3]:

for example in raw_train_ds.take(3):
  print(f"""\
text: {example['text'].numpy()[:200]}
label: {example['label']}
=========================""")

for example in raw_train_ds.take(3): print(f"""\ text: {example['text'].numpy()[:200]} label: {example['label']} =========================""")

text: b"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting "
label: 0
=========================
text: b'I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However '
label: 0
=========================
text: b'Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Moun'
label: 0
=========================

Downloading embedding¶

In [4]:

embed = tensorflow_hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")

embed = tensorflow_hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")

We can test the embedding on any text. It returns a vector of numbers. While those values do not have inherent meaning to us, YDF is very good at consuming them.

In [5]:

embeddings = embed([
    "The little blue dog eats a piece of ham.",
    "It is raining today."]).numpy()
print(embeddings)

embeddings = embed([ "The little blue dog eats a piece of ham.", "It is raining today."]).numpy() print(embeddings)

[[-0.01440776  0.04751815  0.05348268 ...  0.018609    0.03508667
  -0.03631601]
 [-0.0552231  -0.02168638  0.05072879 ...  0.03051734 -0.00266217
   0.01246582]]

Apply embedding on dataset¶

We can apply the embedding to our dataset. Since the dataset and the embedding are both created with TensorFlow, we will prepare a TensorFlow Dataset and feed it directly into YDF. YDF natively consumes TensorFlow Datasets.

In [6]:

def apply_embedding(batch):
    batch["text"] = embed(batch["text"])
    return batch

# The batch-size (256) has not impact on the YDF model. However,
# reading a TensorFlow dataset with a small (<50) batch size might
# be slow. Use a large batch size increases memory usage.
train_ds = raw_train_ds.batch(256).map(apply_embedding)
test_ds = raw_test_ds.batch(256).map(apply_embedding)

def apply_embedding(batch): batch["text"] = embed(batch["text"]) return batch # The batch-size (256) has not impact on the YDF model. However, # reading a TensorFlow dataset with a small (<50) batch size might # be slow. Use a large batch size increases memory usage. train_ds = raw_train_ds.batch(256).map(apply_embedding) test_ds = raw_test_ds.batch(256).map(apply_embedding)

Let's show the first 10 dimensions of the embedding for the 3 examples in the first batch examples.

In [7]:

for example in train_ds.take(1):
  print(f"""\
text: {example['text'].numpy()[:3, :10]}
label: {example['label'].numpy()[:3]}
=========================""")

for example in train_ds.take(1): print(f"""\ text: {example['text'].numpy()[:3, :10]} label: {example['label'].numpy()[:3]} =========================""")

text: [[ 0.04070191  0.00420414 -0.01570062  0.06623042  0.06024029  0.00345815
  -0.00204514 -0.02974475 -0.06150667  0.02128238]
 [ 0.02308333  0.03450448  0.03191734  0.01053793 -0.004009    0.00847429
  -0.03853702 -0.02518811  0.03465953  0.08872268]
 [ 0.0223658  -0.00636589  0.04310491 -0.05726858  0.05173567 -0.02762526
  -0.0447326  -0.00299736 -0.0420398  -0.01994686]]
label: [0 0 0]
=========================

Training a pre-trained embedding model¶

In [8]:

model = ydf.GradientBoostedTreesLearner(label="label").train(train_ds)

model = ydf.GradientBoostedTreesLearner(label="label").train(train_ds)

Train model on 25000 examples
Model trained in 0:00:27.819662

We can observe the 512 dimensions of the embedding. In the "variable importance" tab, we see that not all dimensions of the embedding are equally useful. For example, the feature text.111_of_512 is very useful for the model.

In [9]:

model.describe()

model.describe()

Out[9]:

ModelDataspecTrainingVariable importancesStructure

Name : GRADIENT_BOOSTED_TREES
Task : CLASSIFICATION
Label : label
Features (512) : text.000_of_512 text.001_of_512 text.002_of_512 text.003_of_512 text.004_of_512 text.005_of_512 text.006_of_512 text.007_of_512 text.008_of_512 text.009_of_512 text.010_of_512 text.011_of_512 text.012_of_512 text.013_of_512 text.014_of_512 text.015_of_512 text.016_of_512 text.017_of_512 text.018_of_512 text.019_of_512 text.020_of_512 text.021_of_512 text.022_of_512 text.023_of_512 text.024_of_512 text.025_of_512 text.026_of_512 text.027_of_512 text.028_of_512 text.029_of_512 text.030_of_512 text.031_of_512 text.032_of_512 text.033_of_512 text.034_of_512 text.035_of_512 text.036_of_512 text.037_of_512 text.038_of_512 text.039_of_512 text.040_of_512 text.041_of_512 text.042_of_512 text.043_of_512 text.044_of_512 text.045_of_512 text.046_of_512 text.047_of_512 text.048_of_512 text.049_of_512 text.050_of_512 text.051_of_512 text.052_of_512 text.053_of_512 text.054_of_512 text.055_of_512 text.056_of_512 text.057_of_512 text.058_of_512 text.059_of_512 text.060_of_512 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Weights : None
Trained with tuner : No
Model size : 4220 kB

Number of records: 25000
Number of columns: 513

Number of columns by type:
	NUMERICAL: 512 (99.8051%)
	CATEGORICAL: 1 (0.194932%)

Columns:

NUMERICAL: 512 (99.8051%)
	1: "text.000_of_512" NUMERICAL mean:-0.0116812 min:-0.0802732 max:0.0762275 sd:0.0288812 dtype:DTYPE_FLOAT32
	2: "text.001_of_512" NUMERICAL mean:-0.0138848 min:-0.0922933 max:0.0878271 sd:0.0356528 dtype:DTYPE_FLOAT32
	3: "text.002_of_512" NUMERICAL mean:0.00793811 min:-0.0816057 max:0.104839 sd:0.0379118 dtype:DTYPE_FLOAT32
	4: "text.003_of_512" NUMERICAL mean:0.0173988 min:-0.086644 max:0.0888103 sd:0.0356921 dtype:DTYPE_FLOAT32
	5: "text.004_of_512" NUMERICAL mean:0.0223057 min:-0.0912103 max:0.0840179 sd:0.0366385 dtype:DTYPE_FLOAT32
	6: "text.005_of_512" NUMERICAL mean:0.0112871 min:-0.0888469 max:0.0951167 sd:0.0403517 dtype:DTYPE_FLOAT32
	7: "text.006_of_512" NUMERICAL mean:0.00491578 min:-0.0835639 max:0.0810243 sd:0.0361497 dtype:DTYPE_FLOAT32
	8: "text.007_of_512" NUMERICAL mean:0.00392599 min:-0.0818771 max:0.101976 sd:0.0377445 dtype:DTYPE_FLOAT32
	9: "text.008_of_512" NUMERICAL mean:0.0309414 min:-0.11085 max:0.105491 sd:0.0450593 dtype:DTYPE_FLOAT32
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CATEGORICAL: 1 (0.194932%)
	0: "label" CATEGORICAL has-dict vocab-size:3 zero-ood-items most-frequent:"0" 12500 (50%) dtype:DTYPE_INT64

Terminology:
	nas: Number of non-available (i.e. missing) values.
	ood: Out of dictionary.
	manually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.
	tokenized: The attribute value is obtained through tokenization.
	has-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.
	vocab-size: Number of unique values.

The following evaluation is computed on the validation or out-of-bag dataset.

Task: CLASSIFICATION
Label: label
Loss (BINOMIAL_LOG_LIKELIHOOD): 0.649996

Accuracy: 0.863545  CI95[W][0 1]
ErrorRate: : 0.136455


Confusion Table:
truth\prediction
          0     1
    0  1070   164
    1   174  1069
Total: 2477

Variable importances measure the importance of an input feature for a model.

INV_MEAN_MIN_DEPTHNUM_AS_ROOTNUM_NODESSUM_SCORE

    1. "text.111_of_512"  0.186278 ################
    2. "text.384_of_512"  0.181487 ###########
    3. "text.171_of_512"  0.181331 ###########
    4. "text.022_of_512"  0.178618 ########
    5. "text.305_of_512"  0.177880 #######
    6. "text.459_of_512"  0.177238 #######
    7. "text.386_of_512"  0.175864 #####
    8. "text.408_of_512"  0.175837 #####
    9. "text.307_of_512"  0.175136 #####
   10. "text.182_of_512"  0.175062 #####
   11. "text.353_of_512"  0.174753 ####
   12. "text.308_of_512"  0.174689 ####
   13. "text.302_of_512"  0.174182 ####
   14. "text.132_of_512"  0.173670 ###
   15. "text.281_of_512"  0.173650 ###
   16. "text.006_of_512"  0.173577 ###
   17. "text.402_of_512"  0.173513 ###
   18. "text.023_of_512"  0.173490 ###
   19. "text.237_of_512"  0.173229 ###
   20. "text.284_of_512"  0.173132 ###
   21. "text.245_of_512"  0.173120 ###
   22. "text.419_of_512"  0.173038 ###
   23. "text.361_of_512"  0.172992 ###
   24. "text.167_of_512"  0.172913 ###
   25. "text.352_of_512"  0.172859 ##
   26. "text.276_of_512"  0.172856 ##
   27. "text.056_of_512"  0.172752 ##
   28. "text.173_of_512"  0.172657 ##
   29. "text.201_of_512"  0.172582 ##
   30. "text.038_of_512"  0.172572 ##
   31. "text.300_of_512"  0.172337 ##
   32. "text.090_of_512"  0.172299 ##
   33. "text.232_of_512"  0.172254 ##
   34. "text.065_of_512"  0.172242 ##
   35. "text.342_of_512"  0.172230 ##
   36. "text.155_of_512"  0.172213 ##
   37. "text.079_of_512"  0.172180 ##
   38. "text.251_of_512"  0.172155 ##
   39. "text.064_of_512"  0.172139 ##
   40. "text.041_of_512"  0.172060 ##
   41. "text.482_of_512"  0.172048 ##
   42. "text.157_of_512"  0.171982 ##
   43. "text.082_of_512"  0.171889 ##
   44. "text.017_of_512"  0.171882 ##
   45. "text.452_of_512"  0.171863 ##
   46. "text.439_of_512"  0.171847 #
   47. "text.078_of_512"  0.171789 #
   48. "text.198_of_512"  0.171743 #
   49. "text.328_of_512"  0.171734 #
   50. "text.046_of_512"  0.171713 #
   51. "text.045_of_512"  0.171687 #
   52. "text.035_of_512"  0.171665 #
   53. "text.505_of_512"  0.171613 #
   54. "text.200_of_512"  0.171605 #
   55. "text.190_of_512"  0.171530 #
   56. "text.068_of_512"  0.171527 #
   57. "text.407_of_512"  0.171516 #
   58. "text.314_of_512"  0.171407 #
   59. "text.293_of_512"  0.171403 #
   60. "text.471_of_512"  0.171365 #
   61. "text.063_of_512"  0.171362 #
   62. "text.140_of_512"  0.171356 #
   63. "text.435_of_512"  0.171334 #
   64. "text.443_of_512"  0.171316 #
   65. "text.199_of_512"  0.171314 #
   66. "text.168_of_512"  0.171303 #
   67. "text.350_of_512"  0.171278 #
   68. "text.485_of_512"  0.171273 #
   69. "text.400_of_512"  0.171269 #
   70. "text.379_of_512"  0.171257 #
   71. "text.388_of_512"  0.171223 #
   72. "text.120_of_512"  0.171152 #
   73. "text.043_of_512"  0.171148 #
   74. "text.195_of_512"  0.171125 #
   75. "text.126_of_512"  0.171111 #
   76. "text.192_of_512"  0.171102 #
   77. "text.381_of_512"  0.171092 #
   78. "text.349_of_512"  0.171089 #
   79. "text.150_of_512"  0.171083 #
   80. "text.092_of_512"  0.171076 #
   81. "text.240_of_512"  0.171070 #
   82. "text.204_of_512"  0.171055 #
   83. "text.294_of_512"  0.171029 #
   84. "text.496_of_512"  0.171023 #
   85. "text.133_of_512"  0.170987 #
   86. "text.444_of_512"  0.170980 #
   87. "text.259_of_512"  0.170964 #
   88. "text.370_of_512"  0.170943 #
   89. "text.449_of_512"  0.170939 #
   90. "text.306_of_512"  0.170937 #
   91. "text.212_of_512"  0.170931 #
   92. "text.365_of_512"  0.170931 #
   93. "text.239_of_512"  0.170923 #
   94. "text.442_of_512"  0.170912 #
   95. "text.324_of_512"  0.170890 #
   96. "text.194_of_512"  0.170874 #
   97. "text.317_of_512"  0.170853 #
   98. "text.354_of_512"  0.170842 #
   99. "text.071_of_512"  0.170841 #
  100. "text.269_of_512"  0.170840 #
  101. "text.313_of_512"  0.170810 
  102. "text.179_of_512"  0.170805 
  103. "text.032_of_512"  0.170805 
  104. "text.048_of_512"  0.170804 
  105. "text.188_of_512"  0.170796 
  106. "text.463_of_512"  0.170795 
  107. "text.169_of_512"  0.170791 
  108. "text.364_of_512"  0.170791 
  109. "text.446_of_512"  0.170780 
  110. "text.403_of_512"  0.170771 
  111. "text.415_of_512"  0.170762 
  112. "text.273_of_512"  0.170756 
  113. "text.099_of_512"  0.170752 
  114. "text.346_of_512"  0.170748 
  115. "text.067_of_512"  0.170733 
  116. "text.027_of_512"  0.170721 
  117. "text.238_of_512"  0.170713 
  118. "text.069_of_512"  0.170710 
  119. "text.398_of_512"  0.170702 
  120. "text.351_of_512"  0.170698 
  121. "text.405_of_512"  0.170696 
  122. "text.373_of_512"  0.170694 
  123. "text.425_of_512"  0.170690 
  124. "text.142_of_512"  0.170681 
  125. "text.458_of_512"  0.170665 
  126. "text.296_of_512"  0.170657 
  127. "text.434_of_512"  0.170656 
  128. "text.209_of_512"  0.170652 
  129. "text.031_of_512"  0.170646 
  130. "text.051_of_512"  0.170643 
  131. "text.376_of_512"  0.170634 
  132. "text.494_of_512"  0.170632 
  133. "text.375_of_512"  0.170626 
  134. "text.219_of_512"  0.170621 
  135. "text.176_of_512"  0.170615 
  136. "text.410_of_512"  0.170598 
  137. "text.118_of_512"  0.170594 
  138. "text.217_of_512"  0.170589 
  139. "text.322_of_512"  0.170580 
  140. "text.014_of_512"  0.170561 
  141. "text.500_of_512"  0.170558 
  142. "text.476_of_512"  0.170554 
  143. "text.144_of_512"  0.170545 
  144. "text.246_of_512"  0.170524 
  145. "text.348_of_512"  0.170506 
  146. "text.431_of_512"  0.170504 
  147. "text.059_of_512"  0.170504 
  148. "text.049_of_512"  0.170502 
  149. "text.331_of_512"  0.170502 
  150. "text.344_of_512"  0.170496 
  151. "text.123_of_512"  0.170491 
  152. "text.101_of_512"  0.170490 
  153. "text.089_of_512"  0.170486 
  154. "text.267_of_512"  0.170483 
  155. "text.007_of_512"  0.170477 
  156. "text.347_of_512"  0.170476 
  157. "text.205_of_512"  0.170472 
  158. "text.492_of_512"  0.170467 
  159. "text.382_of_512"  0.170463 
  160. "text.005_of_512"  0.170453 
  161. "text.196_of_512"  0.170438 
  162. "text.076_of_512"  0.170436 
  163. "text.263_of_512"  0.170428 
  164. "text.499_of_512"  0.170426 
  165. "text.436_of_512"  0.170426 
  166. "text.244_of_512"  0.170414 
  167. "text.037_of_512"  0.170410 
  168. "text.149_of_512"  0.170405 
  169. "text.450_of_512"  0.170404 
  170. "text.026_of_512"  0.170395 
  171. "text.272_of_512"  0.170392 
  172. "text.104_of_512"  0.170384 
  173. "text.395_of_512"  0.170381 
  174. "text.030_of_512"  0.170380 
  175. "text.319_of_512"  0.170374 
  176. "text.234_of_512"  0.170373 
  177. "text.297_of_512"  0.170372 
  178. "text.042_of_512"  0.170371 
  179. "text.117_of_512"  0.170370 
  180. "text.093_of_512"  0.170358 
  181. "text.151_of_512"  0.170355 
  182. "text.184_of_512"  0.170353 
  183. "text.424_of_512"  0.170352 
  184. "text.073_of_512"  0.170349 
  185. "text.085_of_512"  0.170347 
  186. "text.044_of_512"  0.170346 
  187. "text.392_of_512"  0.170346 
  188. "text.136_of_512"  0.170345 
  189. "text.224_of_512"  0.170340 
  190. "text.202_of_512"  0.170339 
  191. "text.429_of_512"  0.170328 
  192. "text.369_of_512"  0.170326 
  193. "text.012_of_512"  0.170322 
  194. "text.057_of_512"  0.170317 
  195. "text.274_of_512"  0.170316 
  196. "text.110_of_512"  0.170315 
  197. "text.102_of_512"  0.170312 
  198. "text.172_of_512"  0.170311 
  199. "text.261_of_512"  0.170307 
  200. "text.100_of_512"  0.170300 
  201. "text.241_of_512"  0.170294 
  202. "text.454_of_512"  0.170292 
  203. "text.137_of_512"  0.170289 
  204. "text.428_of_512"  0.170285 
  205. "text.220_of_512"  0.170283 
  206. "text.034_of_512"  0.170283 
  207. "text.165_of_512"  0.170282 
  208. "text.448_of_512"  0.170281 
  209. "text.484_of_512"  0.170277 
  210. "text.467_of_512"  0.170277 
  211. "text.295_of_512"  0.170273 
  212. "text.208_of_512"  0.170273 
  213. "text.095_of_512"  0.170270 
  214. "text.412_of_512"  0.170270 
  215. "text.236_of_512"  0.170263 
  216. "text.039_of_512"  0.170263 
  217. "text.451_of_512"  0.170250 
  218. "text.024_of_512"  0.170248 
  219. "text.112_of_512"  0.170247 
  220. "text.420_of_512"  0.170245 
  221. "text.478_of_512"  0.170237 
  222. "text.456_of_512"  0.170237 
  223. "text.086_of_512"  0.170228 
  224. "text.158_of_512"  0.170227 
  225. "text.124_of_512"  0.170225 
  226. "text.009_of_512"  0.170219 
  227. "text.283_of_512"  0.170214 
  228. "text.290_of_512"  0.170213 
  229. "text.229_of_512"  0.170212 
  230. "text.040_of_512"  0.170211 
  231. "text.228_of_512"  0.170211 
  232. "text.181_of_512"  0.170209 
  233. "text.357_of_512"  0.170206 
  234. "text.162_of_512"  0.170201 
  235. "text.258_of_512"  0.170201 
  236. "text.131_of_512"  0.170198 
  237. "text.474_of_512"  0.170196 
  238. "text.286_of_512"  0.170196 
  239. "text.413_of_512"  0.170192 
  240. "text.203_of_512"  0.170190 
  241. "text.070_of_512"  0.170188 
  242. "text.047_of_512"  0.170187 
  243. "text.215_of_512"  0.170180 
  244. "text.054_of_512"  0.170180 
  245. "text.166_of_512"  0.170178 
  246. "text.481_of_512"  0.170178 
  247. "text.013_of_512"  0.170174 
  248. "text.404_of_512"  0.170174 
  249. "text.187_of_512"  0.170172 
  250. "text.282_of_512"  0.170172 
  251. "text.197_of_512"  0.170171 
  252. "text.316_of_512"  0.170169 
  253. "text.177_of_512"  0.170169 
  254. "text.311_of_512"  0.170168 
  255. "text.389_of_512"  0.170164 
  256. "text.011_of_512"  0.170162 
  257. "text.001_of_512"  0.170162 
  258. "text.417_of_512"  0.170162 
  259. "text.021_of_512"  0.170158 
  260. "text.113_of_512"  0.170152 
  261. "text.265_of_512"  0.170150 
  262. "text.416_of_512"  0.170149 
  263. "text.260_of_512"  0.170149 
  264. "text.135_of_512"  0.170147 
  265. "text.489_of_512"  0.170144 
  266. "text.509_of_512"  0.170143 
  267. "text.360_of_512"  0.170140 
  268. "text.114_of_512"  0.170137 
  269. "text.066_of_512"  0.170135 
  270. "text.119_of_512"  0.170134 
  271. "text.138_of_512"  0.170133 
  272. "text.466_of_512"  0.170133 
  273. "text.028_of_512"  0.170131 
  274. "text.002_of_512"  0.170131 
  275. "text.469_of_512"  0.170130 
  276. "text.122_of_512"  0.170129 
  277. "text.472_of_512"  0.170129 
  278. "text.148_of_512"  0.170127 
  279. "text.227_of_512"  0.170125 
  280. "text.337_of_512"  0.170120 
  281. "text.091_of_512"  0.170118 
  282. "text.097_of_512"  0.170117 
  283. "text.145_of_512"  0.170116 
  284. "text.501_of_512"  0.170112 
  285. "text.355_of_512"  0.170108 
  286. "text.154_of_512"  0.170106 
  287. "text.327_of_512"  0.170102 
  288. "text.053_of_512"  0.170101 
  289. "text.159_of_512"  0.170097 
  290. "text.189_of_512"  0.170096 
  291. "text.473_of_512"  0.170095 
  292. "text.374_of_512"  0.170093 
  293. "text.106_of_512"  0.170092 
  294. "text.108_of_512"  0.170090 
  295. "text.498_of_512"  0.170088 
  296. "text.371_of_512"  0.170087 
  297. "text.210_of_512"  0.170085 
  298. "text.183_of_512"  0.170084 
  299. "text.256_of_512"  0.170083 
  300. "text.074_of_512"  0.170082 
  301. "text.422_of_512"  0.170081 
  302. "text.502_of_512"  0.170079 
  303. "text.487_of_512"  0.170078 
  304. "text.391_of_512"  0.170078 
  305. "text.152_of_512"  0.170078 
  306. "text.312_of_512"  0.170078 
  307. "text.186_of_512"  0.170076 
  308. "text.275_of_512"  0.170076 
  309. "text.287_of_512"  0.170074 
  310. "text.072_of_512"  0.170074 
  311. "text.214_of_512"  0.170070 
  312. "text.363_of_512"  0.170069 
  313. "text.226_of_512"  0.170067 
  314. "text.061_of_512"  0.170067 
  315. "text.242_of_512"  0.170064 
  316. "text.393_of_512"  0.170063 
  317. "text.125_of_512"  0.170063 
  318. "text.504_of_512"  0.170061 
  319. "text.475_of_512"  0.170059 
  320. "text.303_of_512"  0.170058 
  321. "text.087_of_512"  0.170058 
  322. "text.421_of_512"  0.170058 
  323. "text.170_of_512"  0.170054 
  324. "text.289_of_512"  0.170053 
  325. "text.465_of_512"  0.170052 
  326. "text.252_of_512"  0.170051 
  327. "text.008_of_512"  0.170049 
  328. "text.058_of_512"  0.170044 
  329. "text.418_of_512"  0.170044 
  330. "text.310_of_512"  0.170039 
  331. "text.096_of_512"  0.170036 
  332. "text.479_of_512"  0.170034 
  333. "text.341_of_512"  0.170033 
  334. "text.396_of_512"  0.170032 
  335. "text.185_of_512"  0.170030 
  336. "text.015_of_512"  0.170030 
  337. "text.329_of_512"  0.170028 
  338. "text.247_of_512"  0.170027 
  339. "text.430_of_512"  0.170026 
  340. "text.461_of_512"  0.170025 
  341. "text.019_of_512"  0.170025 
  342. "text.483_of_512"  0.170024 
  343. "text.339_of_512"  0.170023 
  344. "text.397_of_512"  0.170020 
  345. "text.262_of_512"  0.170013 
  346. "text.018_of_512"  0.170010 
  347. "text.423_of_512"  0.170008 
  348. "text.163_of_512"  0.170008 
  349. "text.139_of_512"  0.170008 
  350. "text.372_of_512"  0.170007 
  351. "text.401_of_512"  0.170004 
  352. "text.304_of_512"  0.170002 
  353. "text.367_of_512"  0.170000 
  354. "text.161_of_512"  0.170000 
  355. "text.468_of_512"  0.169999 
  356. "text.390_of_512"  0.169999 
  357. "text.174_of_512"  0.169998 
  358. "text.243_of_512"  0.169997 
  359. "text.315_of_512"  0.169996 
  360. "text.511_of_512"  0.169993 
  361. "text.280_of_512"  0.169991 
  362. "text.301_of_512"  0.169991 
  363. "text.083_of_512"  0.169990 
  364. "text.025_of_512"  0.169990 
  365. "text.292_of_512"  0.169990 
  366. "text.207_of_512"  0.169987 
  367. "text.321_of_512"  0.169981 
  368. "text.491_of_512"  0.169981 
  369. "text.075_of_512"  0.169980 
  370. "text.225_of_512"  0.169978 
  371. "text.077_of_512"  0.169977 
  372. "text.129_of_512"  0.169975 
  373. "text.414_of_512"  0.169975 
  374. "text.249_of_512"  0.169973 
  375. "text.221_of_512"  0.169973 
  376. "text.334_of_512"  0.169972 
  377. "text.216_of_512"  0.169971 
  378. "text.175_of_512"  0.169970 
  379. "text.335_of_512"  0.169970 
  380. "text.426_of_512"  0.169965 
  381. "text.147_of_512"  0.169964 
  382. "text.180_of_512"  0.169964 
  383. "text.105_of_512"  0.169962 
  384. "text.368_of_512"  0.169961 
  385. "text.488_of_512"  0.169960 
  386. "text.121_of_512"  0.169959 
  387. "text.178_of_512"  0.169959 
  388. "text.411_of_512"  0.169957 
  389. "text.191_of_512"  0.169956 
  390. "text.213_of_512"  0.169956 
  391. "text.211_of_512"  0.169955 
  392. "text.495_of_512"  0.169954 
  393. "text.103_of_512"  0.169952 
  394. "text.143_of_512"  0.169951 
  395. "text.291_of_512"  0.169951 
  396. "text.359_of_512"  0.169947 
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  160. "text.314_of_512" 17.661533 
  161. "text.149_of_512" 17.626049 
  162. "text.028_of_512" 17.605569 
  163. "text.031_of_512" 17.462220 
  164. "text.162_of_512" 17.402495 
  165. "text.229_of_512" 17.355854 
  166. "text.039_of_512" 17.155819 
  167. "text.324_of_512" 17.068312 
  168. "text.263_of_512" 16.989021 
  169. "text.199_of_512" 16.823877 
  170. "text.108_of_512" 16.818034 
  171. "text.396_of_512" 16.750610 
  172. "text.085_of_512" 16.664209 
  173. "text.499_of_512" 16.548145 
  174. "text.077_of_512" 16.506279 
  175. "text.037_of_512" 16.441114 
  176. "text.217_of_512" 16.201147 
  177. "text.479_of_512" 16.087491 
  178. "text.347_of_512" 16.014063 
  179. "text.296_of_512" 15.917289 
  180. "text.159_of_512" 15.869470 
  181. "text.123_of_512" 15.811632 
  182. "text.449_of_512" 15.754831 
  183. "text.184_of_512" 15.562026 
  184. "text.073_of_512" 15.511483 
  185. "text.294_of_512" 15.496874 
  186. "text.086_of_512" 15.474732 
  187. "text.122_of_512" 15.464668 
  188. "text.183_of_512" 15.443564 
  189. "text.363_of_512" 15.438860 
  190. "text.193_of_512" 15.386683 
  191. "text.236_of_512" 15.318106 
  192. "text.165_of_512" 15.231833 
  193. "text.505_of_512" 15.205128 
  194. "text.295_of_512" 15.182844 
  195. "text.144_of_512" 15.135931 
  196. "text.272_of_512" 15.063292 
  197. "text.344_of_512" 14.938082 
  198. "text.101_of_512" 14.927759 
  199. "text.249_of_512" 14.773932 
  200. "text.187_of_512" 14.773709 
  201. "text.072_of_512" 14.645675 
  202. "text.027_of_512" 14.611296 
  203. "text.444_of_512" 14.502462 
  204. "text.497_of_512" 14.360451 
  205. "text.104_of_512" 14.193867 
  206. "text.484_of_512" 14.094463 
  207. "text.428_of_512" 14.073558 
  208. "text.311_of_512" 14.027835 
  209. "text.047_of_512" 13.774420 
  210. "text.392_of_512" 13.729674 
  211. "text.451_of_512" 13.647917 
  212. "text.478_of_512" 13.525068 
  213. "text.509_of_512" 13.460942 
  214. "text.208_of_512" 13.436668 
  215. "text.024_of_512" 13.425313 
  216. "text.109_of_512" 13.351326 
  217. "text.431_of_512" 13.281562 
  218. "text.415_of_512" 13.257779 
  219. "text.348_of_512" 13.200316 
  220. "text.289_of_512" 13.189908 
  221. "text.261_of_512" 13.179039 
  222. "text.238_of_512" 13.125872 
  223. "text.389_of_512" 13.121540 
  224. "text.357_of_512" 13.084106 
  225. "text.398_of_512" 13.014128 
  226. "text.417_of_512" 12.894288 
  227. "text.013_of_512" 12.791177 
  228. "text.360_of_512" 12.687461 
  229. "text.158_of_512" 12.680247 
  230. "text.106_of_512" 12.614829 
  231. "text.014_of_512" 12.607888 
  232. "text.405_of_512" 12.540806 
  233. "text.054_of_512" 12.533079 
  234. "text.105_of_512" 12.459868 
  235. "text.053_of_512" 12.449502 
  236. "text.391_of_512" 12.403246 
  237. "text.330_of_512" 12.388499 
  238. "text.076_of_512" 12.320446 
  239. "text.110_of_512" 12.267189 
  240. "text.133_of_512" 12.177895 
  241. "text.292_of_512" 12.125224 
  242. "text.244_of_512" 12.117297 
  243. "text.226_of_512" 12.055183 
  244. "text.114_of_512" 11.989928 
  245. "text.487_of_512" 11.967709 
  246. "text.087_of_512" 11.840567 
  247. "text.015_of_512" 11.827488 
  248. "text.151_of_512" 11.792439 
  249. "text.154_of_512" 11.725545 
  250. "text.241_of_512" 11.695179 
  251. "text.121_of_512" 11.677985 
  252. "text.404_of_512" 11.672857 
  253. "text.012_of_512" 11.653154 
  254. "text.002_of_512" 11.547923 
  255. "text.030_of_512" 11.524788 
  256. "text.135_of_512" 11.430597 
  257. "text.057_of_512" 11.369234 
  258. "text.267_of_512" 11.339672 
  259. "text.175_of_512" 11.319826 
  260. "text.413_of_512" 11.240387 
  261. "text.321_of_512" 11.224585 
  262. "text.412_of_512" 11.189550 
  263. "text.161_of_512" 11.162601 
  264. "text.055_of_512" 11.074766 
  265. "text.370_of_512" 11.044554 
  266. "text.020_of_512" 11.025795 
  267. "text.148_of_512" 10.991195 
  268. "text.343_of_512" 10.978523 
  269. "text.395_of_512" 10.940141 
  270. "text.436_of_512" 10.924356 
  271. "text.125_of_512" 10.913189 
  272. "text.066_of_512" 10.893415 
  273. "text.508_of_512" 10.880481 
  274. "text.042_of_512" 10.755142 
  275. "text.220_of_512" 10.738125 
  276. "text.416_of_512" 10.661602 
  277. "text.456_of_512" 10.661070 
  278. "text.005_of_512" 10.502730 
  279. "text.458_of_512" 10.490514 
  280. "text.204_of_512" 10.458747 
  281. "text.019_of_512" 10.450397 
  282. "text.372_of_512" 10.400262 
  283. "text.472_of_512" 10.383116 
  284. "text.441_of_512" 10.317965 
  285. "text.326_of_512" 10.285882 
  286. "text.258_of_512" 10.265295 
  287. "text.506_of_512" 10.250057 
  288. "text.115_of_512" 10.241683 
  289. "text.136_of_512" 10.216008 
  290. "text.225_of_512" 10.182855 
  291. "text.117_of_512" 10.166464 
  292. "text.228_of_512" 10.106576 
  293. "text.492_of_512" 10.066116 
  294. "text.474_of_512" 10.057981 
  295. "text.032_of_512" 10.040702 
  296. "text.248_of_512"  9.938959 
  297. "text.260_of_512"  9.886765 
  298. "text.021_of_512"  9.867193 
  299. "text.401_of_512"  9.828373 
  300. "text.095_of_512"  9.769616 
  301. "text.483_of_512"  9.729457 
  302. "text.050_of_512"  9.706603 
  303. "text.475_of_512"  9.611140 
  304. "text.303_of_512"  9.605214 
  305. "text.211_of_512"  9.538452 
  306. "text.070_of_512"  9.529355 
  307. "text.337_of_512"  9.424925 
  308. "text.124_of_512"  9.424201 
  309. "text.230_of_512"  9.359748 
  310. "text.433_of_512"  9.354902 
  311. "text.119_of_512"  9.319691 
  312. "text.448_of_512"  9.272183 
  313. "text.001_of_512"  9.259415 
  314. "text.210_of_512"  9.249007 
  315. "text.138_of_512"  9.219237 
  316. "text.058_of_512"  9.131830 
  317. "text.469_of_512"  9.125371 
  318. "text.008_of_512"  9.124477 
  319. "text.275_of_512"  9.112806 
  320. "text.083_of_512"  9.050527 
  321. "text.316_of_512"  8.956921 
  322. "text.025_of_512"  8.925180 
  323. "text.476_of_512"  8.902608 
  324. "text.312_of_512"  8.896903 
  325. "text.004_of_512"  8.882758 
  326. "text.280_of_512"  8.829254 
  327. "text.465_of_512"  8.823126 
  328. "text.156_of_512"  8.812926 
  329. "text.461_of_512"  8.747426 
  330. "text.003_of_512"  8.713203 
  331. "text.113_of_512"  8.685859 
  332. "text.393_of_512"  8.644423 
  333. "text.265_of_512"  8.589267 
  334. "text.256_of_512"  8.565640 
  335. "text.495_of_512"  8.511515 
  336. "text.255_of_512"  8.503298 
  337. "text.145_of_512"  8.479545 
  338. "text.293_of_512"  8.454661 
  339. "text.387_of_512"  8.444664 
  340. "text.423_of_512"  8.425266 
  341. "text.166_of_512"  8.422371 
  342. "text.298_of_512"  8.418135 
  343. "text.466_of_512"  8.392731 
  344. "text.253_of_512"  8.387091 
  345. "text.468_of_512"  8.378500 
  346. "text.084_of_512"  8.373563 
  347. "text.223_of_512"  8.300315 
  348. "text.033_of_512"  8.290740 
  349. "text.481_of_512"  8.199431 
  350. "text.186_of_512"  8.167259 
  351. "text.169_of_512"  8.043737 
  352. "text.215_of_512"  7.998535 
  353. "text.341_of_512"  7.990455 
  354. "text.213_of_512"  7.947965 
  355. "text.422_of_512"  7.883293 
  356. "text.000_of_512"  7.873737 
  357. "text.036_of_512"  7.833242 
  358. "text.061_of_512"  7.794338 
  359. "text.304_of_512"  7.791422 
  360. "text.440_of_512"  7.772200 
  361. "text.074_of_512"  7.720522 
  362. "text.325_of_512"  7.701480 
  363. "text.174_of_512"  7.648511 
  364. "text.473_of_512"  7.640396 
  365. "text.116_of_512"  7.432516 
  366. "text.088_of_512"  7.375702 
  367. "text.462_of_512"  7.370414 
  368. "text.141_of_512"  7.358679 
  369. "text.315_of_512"  7.292697 
  370. "text.320_of_512"  7.285890 
  371. "text.052_of_512"  7.263827 
  372. "text.371_of_512"  7.211039 
  373. "text.301_of_512"  7.204238 
  374. "text.390_of_512"  7.184831 
  375. "text.214_of_512"  7.165112 
  376. "text.310_of_512"  7.120250 
  377. "text.152_of_512"  7.114524 
  378. "text.127_of_512"  7.105350 
  379. "text.062_of_512"  7.039762 
  380. "text.143_of_512"  6.995768 
  381. "text.107_of_512"  6.992239 
  382. "text.438_of_512"  6.987579 
  383. "text.131_of_512"  6.976154 
  384. "text.432_of_512"  6.925402 
  385. "text.329_of_512"  6.898728 
  386. "text.147_of_512"  6.843268 
  387. "text.355_of_512"  6.840869 
  388. "text.406_of_512"  6.778595 
  389. "text.446_of_512"  6.766229 
  390. "text.489_of_512"  6.745740 
  391. "text.075_of_512"  6.739690 
  392. "text.218_of_512"  6.688297 
  393. "text.453_of_512"  6.650884 
  394. "text.426_of_512"  6.584627 
  395. "text.335_of_512"  6.569409 
  396. "text.358_of_512"  6.532798 
  397. "text.493_of_512"  6.523429 
  398. "text.007_of_512"  6.411961 
  399. "text.409_of_512"  6.410372 
  400. "text.460_of_512"  6.402201 
  401. "text.011_of_512"  6.397151 
  402. "text.503_of_512"  6.354757 
  403. "text.283_of_512"  6.328816 
  404. "text.366_of_512"  6.263960 
  405. "text.197_of_512"  6.246095 
  406. "text.243_of_512"  6.194773 
  407. "text.421_of_512"  6.105908 
  408. "text.374_of_512"  6.097693 
  409. "text.277_of_512"  6.068572 
  410. "text.332_of_512"  6.067873 
  411. "text.252_of_512"  6.036750 
  412. "text.345_of_512"  6.021022 
  413. "text.191_of_512"  5.988329 
  414. "text.491_of_512"  5.984841 
  415. "text.418_of_512"  5.967021 
  416. "text.203_of_512"  5.944533 
  417. "text.018_of_512"  5.939225 
  418. "text.397_of_512"  5.903827 
  419. "text.139_of_512"  5.829417 
  420. "text.163_of_512"  5.752617 
  421. "text.130_of_512"  5.700992 
  422. "text.231_of_512"  5.688939 
  423. "text.457_of_512"  5.672374 
  424. "text.153_of_512"  5.663403 
  425. "text.368_of_512"  5.651593 
  426. "text.102_of_512"  5.646376 
  427. "text.486_of_512"  5.633483 
  428. "text.216_of_512"  5.629287 
  429. "text.264_of_512"  5.588973 
  430. "text.385_of_512"  5.584986 
  431. "text.490_of_512"  5.562708 
  432. "text.411_of_512"  5.551111 
  433. "text.501_of_512"  5.525986 
  434. "text.016_of_512"  5.516382 
  435. "text.254_of_512"  5.513080 
  436. "text.209_of_512"  5.467364 
  437. "text.498_of_512"  5.420012 
  438. "text.080_of_512"  5.374728 
  439. "text.128_of_512"  5.347706 
  440. "text.359_of_512"  5.270495 
  441. "text.189_of_512"  5.253403 
  442. "text.222_of_512"  5.217164 
  443. "text.221_of_512"  5.178786 
  444. "text.362_of_512"  5.137636 
  445. "text.445_of_512"  5.128978 
  446. "text.177_of_512"  5.116982 
  447. "text.488_of_512"  5.095297 
  448. "text.334_of_512"  5.072061 
  449. "text.430_of_512"  5.053250 
  450. "text.060_of_512"  5.042195 
  451. "text.279_of_512"  5.035647 
  452. "text.378_of_512"  5.022113 
  453. "text.336_of_512"  4.982412 
  454. "text.010_of_512"  4.969288 
  455. "text.480_of_512"  4.966521 
  456. "text.164_of_512"  4.893784 
  457. "text.286_of_512"  4.884769 
  458. "text.323_of_512"  4.852872 
  459. "text.040_of_512"  4.820518 
  460. "text.081_of_512"  4.763537 
  461. "text.333_of_512"  4.742724 
  462. "text.029_of_512"  4.704571 
  463. "text.233_of_512"  4.688918 
  464. "text.207_of_512"  4.668682 
  465. "text.112_of_512"  4.648415 
  466. "text.511_of_512"  4.635224 
  467. "text.146_of_512"  4.626896 
  468. "text.338_of_512"  4.585168 
  469. "text.096_of_512"  4.329233 
  470. "text.287_of_512"  4.263560 
  471. "text.377_of_512"  4.234776 
  472. "text.266_of_512"  4.231089 
  473. "text.206_of_512"  4.208843 
  474. "text.170_of_512"  4.173307 
  475. "text.103_of_512"  4.137199 
  476. "text.094_of_512"  4.077613 
  477. "text.160_of_512"  4.010110 
  478. "text.180_of_512"  4.002525 
  479. "text.185_of_512"  3.965777 
  480. "text.290_of_512"  3.930396 
  481. "text.447_of_512"  3.871413 
  482. "text.380_of_512"  3.856945 
  483. "text.098_of_512"  3.817470 
  484. "text.394_of_512"  3.728094 
  485. "text.234_of_512"  3.596383 
  486. "text.477_of_512"  3.430310 
  487. "text.507_of_512"  3.401816 
  488. "text.278_of_512"  3.371364 
  489. "text.235_of_512"  3.351894 
  490. "text.340_of_512"  3.257311 
  491. "text.437_of_512"  3.248597 
  492. "text.268_of_512"  3.169426 
  493. "text.242_of_512"  3.129669 
  494. "text.414_of_512"  2.998142 
  495. "text.299_of_512"  2.978278 
  496. "text.129_of_512"  2.929291 
  497. "text.470_of_512"  2.814402 
  498. "text.309_of_512"  2.749645 
  499. "text.427_of_512"  2.535817 
  500. "text.318_of_512"  2.409882 
  501. "text.383_of_512"  2.354953 
  502. "text.271_of_512"  2.329468 
  503. "text.455_of_512"  2.312649 
  504. "text.257_of_512"  1.948737 
  505. "text.356_of_512"  1.793478 
  506. "text.262_of_512"  1.436583 
  507. "text.285_of_512"  1.325838 
  508. "text.250_of_512"  0.972889 
  509. "text.270_of_512"  0.908637 
  510. "text.399_of_512"  0.842241 
  511. "text.288_of_512"  0.562196 

Those variable importances are computed during training. More, and possibly more informative, variable importances are available when analyzing a model on a test dataset.

Num trees : 300

Only printing the first tree.

Tree #0:
    "text.111_of_512">=-0.0602566 [s:0.0555518 n:22523 np:11909 miss:1] ; pred:5.56483e-09
        ├─(pos)─ "text.171_of_512">=-0.0261862 [s:0.0197489 n:11909 np:5560 miss:0] ; pred:0.0890043
        |        ├─(pos)─ "text.111_of_512">=0.00956145 [s:0.00532368 n:5560 np:3094 miss:0] ; pred:0.149073
        |        |        ├─(pos)─ "text.065_of_512">=0.0368076 [s:0.00144737 n:3094 np:2189 miss:0] ; pred:0.175128
        |        |        |        ├─(pos)─ "text.219_of_512">=-0.0505817 [s:0.00122259 n:2189 np:2087 miss:1] ; pred:0.184913
        |        |        |        |        ├─(pos)─ pred:0.188005
        |        |        |        |        └─(neg)─ pred:0.121649
        |        |        |        └─(neg)─ "text.302_of_512">=-0.0171091 [s:0.00536794 n:905 np:823 miss:1] ; pred:0.151461
        |        |        |                 ├─(pos)─ pred:0.160712
        |        |        |                 └─(neg)─ pred:0.0586165
        |        |        └─(neg)─ "text.022_of_512">=0.0102688 [s:0.00895105 n:2466 np:1129 miss:0] ; pred:0.116382
        |        |                 ├─(pos)─ "text.408_of_512">=0.0202419 [s:0.00306305 n:1129 np:772 miss:0] ; pred:0.157564
        |        |                 |        ├─(pos)─ pred:0.172619
        |        |                 |        └─(neg)─ pred:0.12501
        |        |                 └─(neg)─ "text.408_of_512">=0.0430553 [s:0.0116265 n:1337 np:495 miss:0] ; pred:0.0816057
        |        |                          ├─(pos)─ pred:0.137858
        |        |                          └─(neg)─ pred:0.048536
        |        └─(neg)─ "text.022_of_512">=-0.0110774 [s:0.023812 n:6349 np:3846 miss:1] ; pred:0.0364006
        |                 ├─(pos)─ "text.182_of_512">=-0.0230205 [s:0.0119394 n:3846 np:2589 miss:1] ; pred:0.0861954
        |                 |        ├─(pos)─ "text.022_of_512">=0.049516 [s:0.00617494 n:2589 np:1240 miss:0] ; pred:0.11665
        |                 |        |        ├─(pos)─ pred:0.149435
        |                 |        |        └─(neg)─ pred:0.0865143
        |                 |        └─(neg)─ "text.410_of_512">=0.00820597 [s:0.0146238 n:1257 np:362 miss:0] ; pred:0.023469
        |                 |                 ├─(pos)─ pred:0.0995275
        |                 |                 └─(neg)─ pred:-0.00729438
        |                 └─(neg)─ "text.171_of_512">=-0.0539957 [s:0.0143023 n:2503 np:1285 miss:1] ; pred:-0.0401119
        |                          ├─(pos)─ "text.384_of_512">=0.0559715 [s:0.0131526 n:1285 np:299 miss:0] ; pred:0.00646125
        |                          |        ├─(pos)─ pred:-0.0768432
        |                          |        └─(neg)─ pred:0.0317229
        |                          └─(neg)─ "text.408_of_512">=0.0233407 [s:0.00972599 n:1218 np:334 miss:0] ; pred:-0.0892469
        |                                   ├─(pos)─ pred:-0.0250698
        |                                   └─(neg)─ pred:-0.113495
        └─(neg)─ "text.408_of_512">=0.0146363 [s:0.0204198 n:10614 np:2509 miss:0] ; pred:-0.0998636
                 ├─(pos)─ "text.022_of_512">=0.0184357 [s:0.0245182 n:2509 np:1137 miss:0] ; pred:0.00286988
                 |        ├─(pos)─ "text.452_of_512">=0.000155947 [s:0.0117618 n:1137 np:442 miss:1] ; pred:0.0716719
                 |        |        ├─(pos)─ "text.182_of_512">=0.00088577 [s:0.0162757 n:442 np:243 miss:0] ; pred:0.0172745
                 |        |        |        ├─(pos)─ pred:0.0634544
                 |        |        |        └─(neg)─ pred:-0.0391161
                 |        |        └─(neg)─ "text.182_of_512">=-0.0310871 [s:0.0106962 n:695 np:597 miss:1] ; pred:0.106267
                 |        |                 ├─(pos)─ pred:0.123028
                 |        |                 └─(neg)─ pred:0.00416156
                 |        └─(neg)─ "text.171_of_512">=-0.0535029 [s:0.0226459 n:1372 np:394 miss:1] ; pred:-0.0541475
                 |                 ├─(pos)─ "text.291_of_512">=0.0608138 [s:0.0252759 n:394 np:318 miss:1] ; pred:0.0406891
                 |                 |        ├─(pos)─ pred:0.0717781
                 |                 |        └─(neg)─ pred:-0.0893938
                 |                 └─(neg)─ "text.305_of_512">=-0.00659941 [s:0.0113343 n:978 np:282 miss:0] ; pred:-0.0923536
                 |                          ├─(pos)─ pred:-0.025452
                 |                          └─(neg)─ pred:-0.11946
                 └─(neg)─ "text.022_of_512">=0.0319392 [s:0.010925 n:8105 np:1696 miss:0] ; pred:-0.131666
                          ├─(pos)─ "text.245_of_512">=0.0584148 [s:0.016193 n:1696 np:644 miss:0] ; pred:-0.0503918
                          |        ├─(pos)─ "text.173_of_512">=0.0489011 [s:0.0113042 n:644 np:64 miss:0] ; pred:-0.115448
                          |        |        ├─(pos)─ pred:0.0125799
                          |        |        └─(neg)─ pred:-0.129575
                          |        └─(neg)─ "text.384_of_512">=0.0196009 [s:0.0159509 n:1052 np:800 miss:1] ; pred:-0.0105665
                          |                 ├─(pos)─ pred:-0.0389201
                          |                 └─(neg)─ pred:0.079445
                          └─(neg)─ "text.245_of_512">=0.0363683 [s:0.00411718 n:6409 np:4305 miss:0] ; pred:-0.153173
                                   ├─(pos)─ "text.171_of_512">=-0.0644174 [s:0.00162702 n:4305 np:1069 miss:1] ; pred:-0.171116
                                   |        ├─(pos)─ pred:-0.143045
                                   |        └─(neg)─ pred:-0.18039
                                   └─(neg)─ "text.171_of_512">=-0.0632608 [s:0.00815962 n:2104 np:628 miss:1] ; pred:-0.11646
                                            ├─(pos)─ pred:-0.0610666
                                            └─(neg)─ pred:-0.140029

Evaluating model¶

We evaluate the model on the test dataset.

In [10]:

model.evaluate(test_ds)

model.evaluate(test_ds)

Out[10]:

Evalution Glossary

Evaluation of classification models

Accuracy

The simplest metric. It's the percentage of predictions that are correct (matching the ground truth).
Example: If a model correctly identifies 90 out of 100 images as cat or dog, the accuracy is 90%.

Confusion Matrix

A table that shows the counts of:

• True Positives (TP): Model correctly predicted positive.
• True Negatives (TN): Model correctly predicted negative.
• False Positives (FP): Model incorrectly predicted positive (a "false alarm").
• False Negatives (FN): Model incorrectly predicted negative (a "miss").

Threshold

YDF classification models predict a probability for each class. A threshold determines the cutoff for classifying something as positive or negative.
Example: If the threshold is 0.5, any prediction above 0.5 might be classified as "spam," and anything below as "not spam."

ROC Curve (Receiver Operating Characteristic Curve)

A graph that plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at various thresholds.

• TPR (Sensitivity or Recall): TP / (TP + FN) - How many of the actual positives did the model catch?
• FPR: FP / (FP + TN) - How many negatives were incorrectly classified as positives?

Interpretation: A good model has an ROC curve that hugs the top-left corner (high TPR, low FPR).

AUC (Area Under the ROC Curve)

A single number that summarizes the overall performance shown by the ROC curve. The AUC is a more stable metric than the accuracy. Multi-class classification models evaluate one class against all other classes.
Interpretation: Ranges from 0 to 1. A perfect model has an AUC of 1, while a random model has an AUC of 0.5. Higher is better.

Precision-Recall Curve

A graph that plots Precision against Recall at various thresholds.

• Precision: TP / (TP + FP) - Out of all the predictions the model labeled as positive, how many were actually positive?
• Recall (same as TPR): TP / (TP + FN) - Out of all the actual positive cases, how many did the model correctly identify?

Interpretation: A good model has a curve that stays high (both high precision and high recall). It is especially useful when dealing with imbalanced datasets (e.g., when one class is much rarer than the other).

PR-AUC (Area Under the Precision-Recall Curve)

Similar to AUC, but for the Precision-Recall curve. A single number summarizing performance. Multi-class classification models evaluate one class against all other classes. Higher is better.

Threshold / Accuracy Curve

A graph that shows how the model's accuracy changes as you vary the classification threshold.

Threshold / Volume Curve

A graph showing how the number of data points classified as positive changes as you vary the threshold.

accuracy:

0.85132

AUC: '1' vs others:

0.931069

PR-AUC: '1' vs others:

0.931735

loss:

0.337104

num examples:

25000

num examples (weighted):

25000

Confusion matrix

Label \ Pred	0	1
0	10724	1941
1	1776	10559

The model accuracy is ~85%. Not too bad for a model trained in a few seconds with default hyper-parameters :)