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predict_log_proba

method predict_log_proba(X, verbose=None)[source]
Get class log-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_log_proba method.

Read more in the user guide.

ParametersX: 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.

Returnsdataframe
Class log-probability predictions with shape=(n_samples, n_classes).

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_log_proba(X_new)

              0          1
0 -6.024211e-10 -21.230064
1 -3.525172e-07 -14.858167
2 -1.285206e-02  -4.360670
3 -6.837442e-11 -23.406023
4 -1.076932e+01  -0.000021

>>> # Using indices
>>> atom.predict_log_proba([23, 25])  # Retrieve prediction of rows 23 and 25

           0         1
23 -4.191844 -0.015234
25 -5.207398 -0.005491