Prediction understanding¶

Setup¶

In [ ]:

pip install ydf -U

pip install ydf -U

In [1]:

import ydf
import pandas as pd

import ydf import pandas as pd

What is prediction understanding?¶

The goal of prediction understanding is to understand a single prediction of a model.

This notebook presents multiple techniques for interpreting the individual predictions of a model trained on the adult dataset.

Gathering dataset and training model¶

In [2]:

ds_path = "https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset"
train_ds = pd.read_csv(f"{ds_path}/adult_train.csv")
test_ds = pd.read_csv(f"{ds_path}/adult_test.csv")

# Print the first 5 training examples
train_ds.head(5)

# Train a model
model = ydf.RandomForestLearner(label="income").train(train_ds)

ds_path = "https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasil_decision_forests/test_data/dataset" train_ds = pd.read_csv(f"{ds_path}/adult_train.csv") test_ds = pd.read_csv(f"{ds_path}/adult_test.csv") # Print the first 5 training examples train_ds.head(5) # Train a model model = ydf.RandomForestLearner(label="income").train(train_ds)

Train model on 22792 examples
Model trained in 0:00:01.194396

Prediction analysis¶

In contrast to model analysis (model.analyze), which examines a model as a whole, prediction analysis (model.analyze_prediction) explains a single prediction of the model. The next example explains the model's prediction on the first test example.

In [3]:

model.analyze_prediction(test_ds.iloc[:1])

model.analyze_prediction(test_ds.iloc[:1])

Out[3]:

Feature Variation

Counterfactual examples¶

Counterfactual examples are the training examples that are most similar to a prediction according to a model. Counterfactual examples can be used to explain the model's prediction by examining the similarities and differences between their features.

For more information, see the standalone counterfactual notebook.