Linear regression and Print MSE and R-square for the training and test data. Of csv file in python
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6. Linear Regression with L2 (Ridge) Regularization def ridge_regression(X, Y, iterations=1000, learning_rate=.01, L2_penalty=1): no_of_training_examples, no_of_features = X.shape np.zeros(no_of_features) for _ in range(iterations): b, W = update_weights(X, Y, no_of_training_examples, learning_rate, L2_penalty) 7. Split the dataset into training and test sets def train_test_split(dataset, split): ### YOUR CODE HERE ### return X_train, Y_train, X_test, Y_test 8. Perform regression algorithm on dataset def evaluate_ridge_regression(dataset, split): X_train, Y_train, X_test, Y_test = functions.train_test_split(dataset, split) # Train the model b, W = functions.ridge_regression(X_train, Y_train, iterations=10000, L2_penalty=0.01) # Make a prediction with the model yhat = functions.predict(X_test, b, W) print("Predicted values:", np.round(yhat[:3], 2)) print("Real values:", Y_test[3]) print("Trained W:", round(W[0], 2)) print("Trained b:", round(b, 2)) visualise(X_test, Y_test, yhat)
Sri K.
Using the same datasets as in problem 2, utilize numpy's polyfit function to find the slope and intercept of the regression line. (the data sets are x = [2.3, 3.1, 4.4, 5.5, 6.1, 7.7, 8.8] and y = [3.2, 4.4, 4.0, 5.9, 6.5, 7.1, 8.0])
Lucas F.
Write a Python function that performs linear regression given the x-data and y-data, with x-data in one list and y-data in another list. There should be an option to force the fitted equation through the origin. The output is/are the coefficient(s) and r^2, the coefficient of determination. Call this function "linear_regression".
Akash M.
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