Pros:

• Simple and easy to train.

• Fast prediction.

• Scales well to very large datasets.

• Works well with sparse data.

• Reasons for prediction are relatively easy to interpret.

Cons:

• For lower-dimensional data, other models may have superior generalization performance.

• For classification, data may not be linearly separable (more on this in SVMs with non-linear kernels)

University of Michigan - Ann Arbor

This is a series of videos that introduces linear regression models and breaks down the process of linear regression.

[StatQuest with Josh Starmer]. (2020, February 21). Linear Regression and Linear Models [Video File]. Retrieved from [www.youtube.com/playlist?list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU](https://www.youtube.com/playlist?list=PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU) 

Linear Regression and Linear Models Videos

Linear models: Pros and Cons

1. alpha: weight given to the L1 or L2 regularization term in regression models. The default value is 1.0
2. C: regularization weight for LinearSVC and LogisticRegression classification models, the default value is 1.0

Important Parameters of linear model

from sklearn.linear_model import LinearRegression

X_train, X_test, y_train, y_test = train_test_split(X_R1, y_R1, random_state = 0)

linreg = LinearRegression().fit(X_train, y_train)

print("linear model intercept (b): {}".format(linreg.intercept_))

print("linear model coeff (w): {}".format(linreg.coef_))

Note: Underscore denotes a quantity derived from training data, as
opposed to a user setting.

Implementation of Linear Regression in Python

The Maximal-Margin Classifier is a hypothetical classifier that best explains how SVM works in practice. The numeric input variables (x) in your data (the columns) form an n-dimensional space. For example, if you had two input variables, this would form a two-dimensional space. In another word, the linear classiifer with max margin is a linear support vector machine

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