Explanation of fit() and predict() Methods

The fit() and predict() methods are crucial parts of building and using a machine learning model, particularly in linear regression.

1. fit() Method: –

The fit() method is used to train the model. – When you call fit(), you provide your input data (also called features or independent variables) and the output data (also called labels or dependent variables). – The model then analyzes the data to find a pattern or relationship between the input and output. – For example, if you have data on the year and corresponding per capita income, the fit() method will learn how the income changes over the years. – The model finds the best line (in case of linear regression) that fits the data points. – This line can be represented by the equation y = mx + b, where m is the slope and b is the intercept. The model determines these values during the fitting process.

2. predict() Method: –

After training the model using fit(), you can use the predict() method to make predictions on new data. – You give the model new input data (like a future year), and it uses the learned pattern to predict the output (like the per capita income for that year). – The model applies the equation it learned during fit() to the new data to make the prediction. – For example, if you input the year 2030, the predict() method will estimate the per capita income for that year based on the pattern it learned.