predict_proba
method predict_proba(X, verbose=None)[source]
Get class probabilities on new data or rows in the dataset.
New data is first transformed through the model's pipeline.
Transformers that are only applied on the training set are
skipped. The estimator must have a predict_proba method.
Read more in the user guide.
| Parameters | X: int, str, slice, sequence or dataframe-like
Names or indices of rows in the dataset, or new
feature set with shape=(n_samples, n_features).
verbose: int or None, default=None
Verbosity level of the output. If None, it uses the
transformer's own verbosity.
|
| Returns | dataframe
Class probability predictions with shape=(n_samples, n_classes)
or (n_targets * n_samples, n_classes) with a multiindex format
for multioutput tasks.
|
Example
>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer
>>> # Load data and separate last 5 rows for predictions
>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)
>>> X_new, y_new = X.iloc[-5:], y.iloc[-5:]
>>> X, y = X.iloc[:-5], y.iloc[:-5]
>>> atom = ATOMClassifier(data)
>>> atom.run("LR")
>>> # Using new data
>>> atom.predict_proba(X_new)
0 1
0 1.000000 4.036791e-10
1 1.000000 4.856420e-07
2 0.981879 1.812090e-02
3 1.000000 6.081561e-11
4 0.000025 9.999746e-01
>>> # Using indices
>>> atom.predict_proba([23, 25]) # Retrieve prediction of rows 23 and 25
0 1
23 0.000892 0.999108
25 0.975733 0.024267