Monday , July 22 2024

Support Vector Regression

Support Vector Regression (SVR) is a regression technique that uses support vector machines to predict continuous numeric values. It’s particularly useful when dealing with non-linear and complex datasets. In Python, you can implement SVR using libraries such as Scikit-Learn. Below is a step-by-step guide on how to perform SVR in Python:

Step 1: Import Libraries

import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler

Step 2: Prepare Your Data
You should have your dataset ready, with independent features (X) and the corresponding target variable (y). Ensure that the data is in a NumPy array or a DataFrame.

Step 3: Feature Scaling
SVR is sensitive to the scale of input features, so it’s essential to perform feature scaling. Use the StandardScaler from Scikit-Learn to standardize your data.

sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y.reshape(-1, 1)).ravel()

Step 4: Create the SVR Model

svr = SVR(kernel='rbf')  # You can choose different kernels like 'linear', 'poly', or 'sigmoid'

Step 5: Train the SVR Model

svr.fit(X, y)

Step 6: Make Predictions

y_pred = svr.predict(X)

Step 7: Visualize the Results (Optional)
You can plot the actual values and the predicted values to visualize how well the SVR model performs.

plt.scatter(X, y, color='red', label='Actual')
plt.plot(X, y_pred, color='blue', label='Predicted')
plt.title('SVR Prediction')
plt.xlabel('X-axis')
plt.ylabel('y-axis')
plt.legend()
plt.show()

Remember that this is a basic example of using SVR in Python. In practice, you may need to tune hyperparameters, perform cross-validation, and evaluate the model’s performance using metrics like Mean Squared Error (MSE) or R-squared (R²).

Also, it’s crucial to split your dataset into training and testing subsets to assess the model’s generalization performance. You can use Scikit-Learn’s train_test_split function for this purpose.

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