Machine Learning Models
1.Machine learning models can be classified into the following two categories on the basis of the learning algorithm:
- Supervised learning method: Past data with labels is available to build the model.
- Regression: The output variable is continuous in nature.
- Classification: The output variable is categorical in nature.
- Unsupervised learning method: Past data with labels is not available.
- Clustering: There is no predefined notion of labels.
Past dataset is divided into two parts in the supervised learning method:
- Training data is used for the model to learn during modelling.
- Testing data is used by the trained model for prediction and model evaluation.
- Linear regression models can be classified into two types depending upon the number of independent variables:
- Simple linear regression: This is used when the number of independent variables is 1.
- Multiple linear regression: This is used when the number of independent variables is more than 1.
- The equation of the best fit regression line Y = β₀ + β₁X can be found by minimising the cost function (RSS in this case, using the ordinary least squares method), which is done using the following two methods:
- Gradient descent
- The strength of a linear regression model is mainly explained by R², where R² = 1 — (RSS/TSS).
- RSS: Residual sum of squares
- TSS: Total sum of squares
- RSE helps in measuring the lack of fit of a model on a given data. The closeness of the estimated regression coefficients to the true ones can be estimated using RSE. It is related to RSS by the formula: RSE=√RSSdf, where df=n−2 and n is the number of data points.