Useful utility functions for evaluation of ML
Useful utility functions for evaluation of ML.
Motivation: The main motivation behind this package is to create a single place where the utility functions for ML projects are located. These functions represent the best I was able to scrape from various tutorials or offical documentation on the web.
This function prints and plots the confusion matrix.
import numpy as np from ddplt import plot_confusion_matrix y_test = np.array([0, 0, 1, 1, 2, 0]) y_pred = np.array([0, 1, 1, 2, 2, 0]) class_names = np.array(['hip', 'hop', 'pop']) ax, cm = plot_confusion_matrix(y_test, y_pred, class_names)
will create a plot like:
Create plot showing performance evaluation for different sizes of training data. The method should accept:
- performance measure (e.g. accuracy, MSE, precision, recall, etc.)
Plot showing Receiver Operating Characteristics of a predictor.
Grid where each square has a color denoting strength of a correlation between predictors. You can choose between Pearson and Spearman correlation coefficient, the result is shown inside the square.
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