When should you use a linear model for machine learning?

When should you use a linear model? Linear models are useful when the relationship between the independent and dependent variables is linear. For example, if you're trying to predict a person's height based on their age, a linear model would be appropriate. Linear models assume that the data is normally distributed, so if the data is not normally distributed, a non-linear model may be more appropriate. Linear models are useful when there are few independent variables. If there are many independent variables, a non-linear model may be more appropriate. Linear models are useful when the cost of false positives is low. If the cost of false positives is high, a non-linear model may be more appropriate.

Advantages of linear ML models: 1. Interpretability: Simple and easy to interpret. 2. Computational Efficiency: Efficient for large datasets. 3. Scalability: Scales well to high-dimensional datasets. 4. No Overfitting Risk: Less prone to overfitting with limited features. 5. Ease of Implementation: Easy to implement and deploy. 6. Statistical Inference: Strong connection to statistical methods.

Limitations of linear models: 1. Limited Complexity: Struggle with nonlinear patterns. 2. Oversimplification: May oversimplify real-world relationships. 3. Outlier Sensitivity: Sensitive to outliers, impacting performance. 4. Assumption of Independence: Assumes independent features. 5. Not for Nonlinear Data: Inaccurate predictions for nonlinear relationships. 6. Limited Feature Importance: May not capture complex feature interactions effectively.

When analyzing the independent variable (output) of the machine learning problem, if it seems like it has a categorical output (ex: when the problem is a lung cancer detection, a person has mostly two status of output as YES, NO ) or a continuous output (ex: quantity, money, height, weight) it is good to go with Linear regression.

1) When the relationship between the input features and the target variable appears to be approximately linear. check Pearson correlation coefficient or the coefficient of determination (R-squared). 2) When simplicity and interpretability are important. 3) Used as baselines for comparison with more complex models. 4) Linear models are computationally efficient and can handle large datasets with many features. 5) Linear models can be regularized to prevent overfitting and improve generalization performance. Techniques such as L1 (Lasso) and L2 (Ridge) regularization can help in handling multicollinearity and reducing model complexity. 6)Linear models can handle feature engineering effectively, such as feature scaling and transformation.

A linear model must not be confused with the straight-line graph of a linear regression model. The term 'linear model' refers to the concept that when the inputs are multiplied by a constant factor, the output also changes proportionally. For example, in a house price prediction model, if the number of bedrooms is doubled, the predicted price also doubles because the weights associated with the features are simply multiplied by the input values. But that is not how house pricing works in the real world. However, this simplistic 'linear' relationship can be hindered or modified using techniques such as feature engineering and kernels.

You use Linear Regression Model when the target or the output to be predicted is a continuous number. This implies that the target is not binary (0 or 1) but a continuous numbers like salary, insurance amount to charge, loan amount to disburse etc.