How to use SVM Classifier and Regressor in Python?
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How to use SVM Classifier and Regressor in Python?

How to use SVM Classifier and Regressor in Python?

This recipe helps you use SVM Classifier and Regressor in Python

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Recipe Objective

Have you ever tried to use SVM (support vector machine) models ie. regressor or classifier. In this we will using both for different dataset.

So this recipe is a short example of how we can use SVM Classifier and Regressor in Python.

Step 1 - Import the library

from sklearn import datasets from sklearn import metrics from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import seaborn as sns plt.style.use("ggplot") from sklearn.svm import SVC, SVR

Here we have imported various modules like datasets, SVC, SVR and test_train_split from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.

Step 2 - Setup the Data for classifier

Here we have used datasets to load the inbuilt cancer dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_breast_cancer() X = dataset.data y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Step 3 - Model and its Score

Here, we are using Support Vector Classifier (SVC) as a Machine Learning model to fit the data. model = SVC() model.fit(X_train, y_train) print(model) Now we have predicted the output by passing X_test and also stored real target in expected_y. expected_y = y_test predicted_y = model.predict(X_test) Here we have printed classification report and confusion matrix for the classifier. print(metrics.classification_report(expected_y, predicted_y)) print(metrics.confusion_matrix(expected_y, predicted_y))

Step 4 - Setup the Data for regressor

Here we have used datasets to load the inbuilt boston dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_boston() X = dataset.data y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Step 5 - Model and its Score

Here, we are using Support Vector Regressor (SVR) as a Machine Learning model to fit the data. model = SVR() model.fit(X_train, y_train) print(model) Now we have predicted the output by passing X_test and also stored real target in expected_y. expected_y = y_test predicted_y = model.predict(X_test) Here we have printed r2 score and mean squared log error for the Regressor. print(metrics.r2_score(expected_y, predicted_y)) print(metrics.mean_squared_log_error(expected_y, predicted_y)) plt.figure(figsize=(10,10)) sns.regplot(expected_y, predicted_y, fit_reg=True, scatter_kws={"s": 100})

As an output we get:

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape="ovr", degree=3, gamma="auto_deprecated",
  kernel="rbf", max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        47
           1       0.67      1.00      0.80        96

   micro avg       0.67      0.67      0.67       143
   macro avg       0.34      0.50      0.40       143
weighted avg       0.45      0.67      0.54       143


[[ 0 47]
 [ 0 96]]

SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,
  gamma="auto_deprecated", kernel="rbf", max_iter=-1, shrinking=True,
  tol=0.001, verbose=False)

-0.014799656287679985

0.16697303856023255

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