Collinearity occurs when two predictor variables (e.g., x1 and x2) in a multiple regression have some correlation.
Multicollinearity occurs when more than two predictor variables (e.g., x1, x2, and x3) are inter-correlated.

In the context of machine learning and statistics, collinearity and multicollinearity refer to the presence of strong correlations between predictor variables in a regression model.

Collinearity: Collinearity occurs when two or more predictor variables in a regression model are highly correlated with each other. This means that there is a linear relationship between the predictor variables. Collinearity can make it difficult for the model to estimate the individual effects of each predictor variable on the outcome variable because the effects become confounded.
Multicollinearity: Multicollinearity is a specific type of collinearity that occurs when three or more predictor variables in a regression model are highly correlated. Multicollinearity can cause issues with the interpretation of regression coefficients. It can also lead to unstable coefficient estimates, making it challenging to assess the significance of individual predictor variables.

Both collinearity and multicollinearity can inflate the variance of coefficient estimates, making them less reliable. They can also make it difficult to identify the most important predictor variables in a model and can lead to misleading conclusions about the relationships between variables.

To address collinearity and multicollinearity, techniques such as principal component analysis (PCA), ridge regression, or LASSO regularization can be employed to reduce the impact of correlated predictor variables and improve the stability and interpretability of the regression model.