calc_feature_statistics

Calculate and plot a set of statistics for the chosen feature.

An example of plotted statistics:

The X-axis of the resulting chart contains values of the feature divided into buckets. For numerical features, the splits between buckets represent conditions (feature < value) from the trees of the model. For categorical features, each bucket stands for a category.

The Y-axis of the resulting chart contains the following graphs:

  • Average target (label) value in the bucket.

  • Average prediction in the bucket.

  • Number of objects in the bucket.

  • Average predictions on varying values of the feature.

    To calculate it, the value of the feature is successively changed to fall into every bucket for every input object. The value for a bucket on the graph is calculated as the average for all objects when their feature values are changed to fall into this bucket.

The return value of the function contains the data from these graps.

The following information is used for calculation:

  • Trained model
  • Dataset
  • Label values

Alert

  • Only models with one-hot encoded categorical features are supported. Set the one_hot_max_size parameter to a large value to ensure that one-hot encoding is applied to all categorical features in the model.
  • Multiclassification modes are not supported.

Method call format

calc_feature_statistics(data,
                        target=None,
                        feature=None,
                        prediction_type=None,
                        cat_feature_values=None,
                        plot=True,
                        max_cat_features_on_plot=10,
                        thread_count=-1,
                        plot_file=None)

Parameters

data

Description

The data to calculate the statistics on.

Possible types

  • numpy.ndarray
  • pandas.DataFrame
  • pandas.SparseDataFrame

  • scipy.sparse.spmatrix (all subclasses except dia_matrix)

Default value

Required parameter

target

Description

Label values for objects from the input data.

Possible types

  • numpy.ndarray
  • pandas.Series

Default value

Required parameter

feature

Description

The feature index, name or a list of any their combination to calculate the statistics for.

Usage examples

Output information regarding a single feature indexed 0:

feature=0

Output information regarding two features, one of which is named age and the second is indexed 10:

feature=["age", 10]

Possible types:

  • int
  • string
  • list of int, string or their combination

Default value
Required parameter

prediction_type

Description

The prediction type to calculate the mean prediction.

Possible values:

  • Probability
  • Class
  • RawFormulaVal
  • Exponent
  • LogProbability

Possible types

string

Default value

None (Probability for Logloss and CrossEntropy, RawFormulaVal for all other loss functions)

cat_feature_values

Description

A list of categorical feature values to calculate the statistics on. Can be used if statistics are calculated for a categorical feature.

Possible types

  • list
  • numpy.ndarray
  • pandas.Series

Default value
None (statistics are calculated for all values of categorical feature)

plot

Description

Plot a Jupyter Notebook chart based on the calculated statistics.

Possible types

bool

Default value

True

max_cat_features_on_plot

Description

The maximum number of different values of a categorical feature to output on one chart.

If the selected categorical feature takes more different values than the value set in this parameter, several charts are plotted.

Possible types

int

Default value

10

thread_count

Description

The number of threads to use for calculating statistics.

Possible types

int

Default value

-1 (the number of threads is equal to the number of processor cores)

plot_file

Description

The name of the output file to save the chart to.

Possible types string

Default value None (the file is not saved)

Usage examples

from catboost import CatBoostRegressor
import numpy as np

train_data = np.random.rand(200, 10)
label_values = np.random.rand(200)

model = CatBoostRegressor()
model.fit(train_data, label_values)

res = model.calc_feature_statistics(train_data,
                                    label_values,
                                    feature=2,
                                    plot=True)

The following is a chart plotted with Jupyter Notebook for the given example.

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