Train a model in Python from start to finish

January 12, 2022

When it comes down to training and assessing a machine learning model in Python, the process tends to be pretty standard. The individual steps usually include: Accessing the data Preparing the data as appropriate. This step is highly dependent on the particular situation. Splitting the data into training, testing, and validation sets Training the model Tuning the model iteratively based on how it performing against the testing data Assessing the model against the validation data by examining the accuracy, confusion matrix, and other metrics reflected in a detailed report Identifying the most important features. ...