Grid and randomized search
Apr 4, 2022
The process of learning a predictive model is driven by a set of internal parameters and a set of training data. These internal parameters are called hyperparameters and are specific for each family of models. In addition, a specific set of hyperparameters are optimal for a specific dataset and thus they need to be optimized.
Mar 8, 2022
Mar 7, 2022
Dealing with categorical variables by encoding them, namely ordinal encoding and one-hot encoding
Feb 24, 2022
Dealing with categorical variables by encoding them, namely ordinal encoding and one-hot encoding
Feb 24, 2022
how to build predictive models on tabulardatasets, with only numerical features
Feb 16, 2022
how to build predictive models on tabulardatasets, with only numerical features
Feb 16, 2022
Look at the data set
Feb 16, 2022
how to build predictive models on tabulardatasets, with only numerical features
Feb 16, 2022
how to build predictive models on tabulardatasets, with only numerical features
Feb 16, 2022
A linear regression model minimizes the mean squared error on the training set.
Sep 7, 2021
How to optimize hyperparameters using a grid-search or random approach
Sep 3, 2021
We recall that hyperparameters refer to the parameter that will control the learning process.
Aug 3, 2021
Validation and learning curves
Jul 22, 2021
We will show how to combine preprocessing steps on numerical and categorical
Jun 8, 2021
