simplefit.classifier
Module Contents
Functions
|
This function preprocess the data, fit baseline model(dummyclassifier) and logistic regression with default setups to provide data scientists |
- simplefit.classifier.classifier(train_df, target_col, numeric_feats=None, categorical_feats=None, cv=5)[source]
This function preprocess the data, fit baseline model(dummyclassifier) and logistic regression with default setups to provide data scientists easy access to the common models results(scores).
- Parameters:
train_df (pandas.DataFrame) – The clean train data which includes target column.
target_col (str) – The column of the train data that has the target values.
list (numeric_feats =) – The numeric features that needs to be considered in the model. If the user enters an empty list, the function will use all numeric columns.
categorical_feats (list) – The categorical features that needs to be considered in the model.
cv (int, optional) – The number of folds on the data for train and validation set.
- Returns:
A data frame that includes test scores and train scores for each model.
- Return type:
Data frame
Examples
>>> classifier(train_df, target_col = 'target', numerical_feats = [], categorical_features = []) >>> classifier(train_df, target_col = 'target', numeric_feats = ['danceability', 'loudness'], categorical_feats=['genre'], cv=10)