We have followed the same step of creating a chart as earlier examples. The third chart type that we'll explain is the ROC AUC curve chart. The show method has a parameter named output_path where we can path and image name if we want to store image to disk. If we want to generate a confusion matrix for train data then we need to call the score() method with train data. Below we have called the score() method with the test dataset. Please make a note that the show() method will always show charts based on the data set on which the score() method was called. We have generated a confusion matrix of digits test data and used a random forest sklearn estimator. We can then simply call the show() method on this object and it'll create a confusion matrix of test data. We can then call the fit() and score() method on the object of class ConfusionMatrix which will train model passed to it on train data and evaluate it on test data. We'll first need to create an object of this class passing it machine learning model. The classifier module of yellowbrick has a class named ConfusionMatrix which lets us create a confusion matrix chart. The first chart that we'll introduce is a confusion matrix plot. shape ) X_train_digits, X_test_digits, Y_train_digits, Y_test_digits = train_test_split ( X_digits, Y_digits, train_size = 0.80, stratify = Y_digits, random_state = 123 ) print ( "Digits Train/Test Sizes : ", X_train_digits. target print ( "Digits Dataset Size : ", X_breast_can. shape ) print () X_digits, Y_digits = digits. shape ) X_train_breast_can, X_test_breast_can, Y_train_breast_can, Y_test_breast_can = train_test_split ( X_breast_can, Y_breast_can, train_size = 0.80, stratify = Y_breast_can, random_state = 123 ) print ( "Breast Cancer Train/Test Sizes : ", X_train_breast_can. target print ( "Breast Cancer Dataset Size : ", X_breast_can. shape ) print () X_breast_can, Y_breast_can = breast_cancer. shape ) X_train_wine, X_test_wine, Y_train_wine, Y_test_wine = train_test_split ( X_wine, Y_wine, train_size = 0.80, stratify = Y_wine, random_state = 123 ) print ( "Wine Train/Test Sizes : ", X_train_wine. target print ( "Wine Dataset Size : ", X_wine. This reduces dimensionality and gives invariance to smallįrom sklearn.model_selection import train_test_split X_wine, Y_wine = wine. This generatesĪn input matrix of 8x8 where each element is an integer in the rangeĠ.16. 32x32 bitmaps are divided into nonoverlapping blocks ofĤx4 and the number of on pixels are counted in each block. Total of 43 people, 30 contributed to the training set and different 13 Normalized bitmaps of handwritten digits from a preprinted form. Preprocessing programs made available by NIST were used to extract The data set contains images of hand-written digits: 10 classes where This is a copy of the test set of the UCI ML hand-written digits datasets :Attribute Information: 8x8 image of integer pixels in the range 0.16. We'll start by importing the necessary libraries. We'll be explaining how to use yellowbrick API as a part of this tutorial with primarily concentrating on visualizing classification and regression task metrics. Yellowbrick has different modules for tasks like feature visualizations, classification task metrics visualizations, regression task metrics visualizations, clustering task metrics visualizations, model selection visualizations, text data related visualizations, etc. Yellowbrick is a python library that provides various modules to visualize model evaluation metrics. Though scikit-learn provides extensive models and metrics to evaluate those models, it does not provide functionalities to visualize that model evaluation metrics. Apart from that scikit-learn even provides functionalities related to feature selection, feature extraction, dimensionality reduction, grid searching hyper-parameters, etc. Scikit-learn provides a very easy-to-use interface which lets us build an ML model using python with few lines of codes. A library like scikit-learn has earned a reputation of the go-to library for ML models by the majority of data scientists and machine learning practitioners. Python has many libraries that let us build machine learning models easily with a few lines of code. Yellowbrick - Visualize Sklearn's Classification & Regression Metrics in Python ¶ Table of Contents ¶
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