Decision Tree intuition

Lecture 39:- Decision Tree intuition

Here's a Python code example using scikit-learn to implement Support Vector Regression (SVR):

pythonCopy code

import numpy as np import matplotlib.pyplot as plt from sklearn.svm import SVR # Example data x = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)  # Reshape to a column vector y = np.array([2, 3, 4, 2, 3]) # Create and fit the SVR model model = SVR(kernel='linear', C=1.0)  # Linear kernel with C=1.0 (regularization parameter) model.fit(x, y) # Predict using the model x_new = np.array([6, 7, 8]).reshape(-1, 1) predictions = model.predict(x_new) # Visualize the SVR Model plt.scatter(x, y, color='blue', label='Data Points') plt.plot(x, model.predict(x), color='red', label='SVR Model') plt.scatter(x_new, predictions, color='green', label='Predictions') plt.xlabel('Independent Variable (x)') plt.ylabel('Dependent Variable (y)') plt.title('Support Vector Regression (SVR) using scikit-learn') plt.legend() plt.show() print("Predictions on new data:", predictions)

In this implementation, we used scikit-learn's SVR class to create and train the SVR model. The process is as follows:

We imported the necessary libraries, including numpy, matplotlib.pyplot, and SVR from sklearn.svm.

We defined the example data x and y.

We created an instance of SVR as model with a linear kernel and regularization parameter C set to 1.0.

We fitted the model with the data using model.fit(x, y).

We then made predictions on new data x_new using the model.

Finally, we used matplotlib to visualize the SVR model's curve, data points, and predictions.

In this example, we used the linear kernel (kernel='linear') to perform linear regression with SVR. However, you can also try other kernels like the radial basis function (RBF) kernel (kernel='rbf'), polynomial kernel (kernel='poly'), or sigmoid kernel (kernel='sigmoid'). The choice of kernel and hyperparameters will depend on the nature of the data and the complexity of the underlying relationship between variables. It's essential to tune these hyperparameters appropriately for the best model performance.