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