API Reference¶
Classification Plots¶
Plots to evaluate classification models.
Module containing all classification model evaluation plots
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mlplot.classification.
calibration
(y_true, y_pred, ax=None, n_bins='auto')¶ Plot a calibration plot
Calibration plots are used the determine how well the predicted values match the true value.
This plot is as found in sklean.
Parameters: - y_true (np.array of str or int) – A vector of size N that contains the true labels. There should be two labels of type string or numeric.
- y_pred (np.array of float) – A vector of size N that contains predictions as floats from 0 to 1.
- class_labels (dict, optional) – A dictionary mapping from lables in y_true to class names. Ex: {0: ‘not dog’, 1: ‘is dog’}
- ax (matplotlib.axes.Axes, optional)
- n_bins (int or string) – The number of bins to group y_pred. See numpy.histogram
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mlplot.classification.
confusion_matrix
(y_true, y_pred, class_labels=None, threshold=0.5, ax=None)¶ Plot a heatmap for the confusion matrix
An example of this heatmap can be found on sklean.
Parameters: - y_true (np.array of str or int) – A vector of size N that contains the true labels. There should be two labels of type string or numeric.
- y_pred (np.array of float) – A vector of size N that contains predictions as floats from 0 to 1.
- class_labels (dict, optional) – A dictionary mapping from lables in y_true to class names. Ex: {0: ‘not dog’, 1: ‘is dog’}
- ax (matplotlib.axes.Axes, optional)
- threshold (float) – Defines the cutoff to be considered in the asserted class
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mlplot.classification.
population_histogram
(y_true, y_pred, class_labels=None, ax=None)¶ Plot histograms of the predictions grouped by class
Parameters: - y_true (np.array of str or int) – A vector of size N that contains the true labels. There should be two labels of type string or numeric.
- y_pred (np.array of float) – A vector of size N that contains predictions as floats from 0 to 1.
- class_labels (dict, optional) – A dictionary mapping from lables in y_true to class names. Ex: {0: ‘not dog’, 1: ‘is dog’}
- ax (matplotlib.axes.Axes, optional)
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mlplot.classification.
precision_recall
(y_true, y_pred, x_axis='recall', ax=None)¶ Plot the precision-recall curve
An example of this plot can be found on sklean.
Parameters: - y_true (np.array of str or int) – A vector of size N that contains the true labels. There should be two labels of type string or numeric.
- y_pred (np.array of float) – A vector of size N that contains predictions as floats from 0 to 1.
- class_labels (dict, optional) – A dictionary mapping from lables in y_true to class names. Ex: {0: ‘not dog’, 1: ‘is dog’}
- ax (matplotlib.axes.Axes, optional)
- x_axis (str ‘recall’ or ‘threshold’) – Specify the x axis of the plot. Precision recall tends to come in 2 flavors, one precision vs recall and the other precion and recall vs threshold.
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mlplot.classification.
report_table
(y_true, y_pred, class_labels=None, ax=None)¶ Generate a report table containing key stats about the dataset
Parameters: - y_true (np.array of str or int) – A vector of size N that contains the true labels. There should be two labels of type string or numeric.
- y_pred (np.array of float) – A vector of size N that contains predictions as floats from 0 to 1.
- class_labels (dict, optional) – A dictionary mapping from lables in y_true to class names. Ex: {0: ‘not dog’, 1: ‘is dog’}
- ax (matplotlib.axes.Axes, optional)
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mlplot.classification.
roc_curve
(y_true, y_pred, ax=None)¶ Reciever operating curve
Parameters: - y_true (np.array of str or int) – A vector of size N that contains the true labels. There should be two labels of type string or numeric.
- y_pred (np.array of float) – A vector of size N that contains predictions as floats from 0 to 1.
- class_labels (dict, optional) – A dictionary mapping from lables in y_true to class names. Ex: {0: ‘not dog’, 1: ‘is dog’}
- ax (matplotlib.axes.Axes, optional)