fitsearchcv 1.0.1

A smarter refit to sklearn.GridSearchCV and sklearn.RandomizedSearchCV to reduce over and underfitting

FitSearchCV— A smarter refit selector for scikit-learn searches

selector-mean is a tiny utility that helps reduce overfitting and underfitting when tuning hyperparameters with scikit-learn’s GridSearchCV or RandomizedSearchCV.

It provides a single function:

• selector_mean(cv_results_, metric=None, use_abs_gap=True, clip01=True)
  A callable you pass to refit=... that picks the parameter set balancing high test performance and small train–test gap.

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Best Use cases

-Accuracy, Balanced Accuracy

-Precision, Recall, F1, Jaccard similarity

-ROC AUC, PR AUC

-Matthews correlation coefficient (MCC, normalized variant)

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Why?

Vanilla GridSearchCV usually selects the highest mean test score, which can sometimes favor models with high variance.
selector_mean instead minimizes: 0.5 * (|train - test|) + 0.5 * (1 - test)
This prevents both underfittnig and overfitting.

|train-test| is for reducing the gap between train and test accuracy thus decreasing overfitting.

(1-test) is for reducing the gap between test accuracy and 1 hence increasing the score thus reducing underfitting.

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Want to try?

Just type

pip install fitsearhcv

Github link: Github

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How to Use?

from fitsearchcv.selectors import selector_mean
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression

lr=LogisticRegression()

param_grid = [
    {'penalty': ['l1'], 'C': [0.1, 1, 10], 'solver': ['liblinear', 'saga']},
    
    {'penalty': ['l2'], 'C': [0.1, 1, 10], 'solver': ['liblinear', 'lbfgs', 'saga', 'sag', 'newton-cg']},
    
    {'penalty': ['elasticnet'], 'C': [0.1, 1, 10], 'solver': ['saga'], 'l1_ratio': [0.0, 0.25, 0.5, 0.75, 1.0]},
    
    {'penalty': [None], 'solver': ['lbfgs', 'sag', 'newton-cg', 'saga']}  
]

grid1=GridSearchCV(estimator=lr,
                   param_grid=param_grid,
                   refit=selector_mean,
                   cv=5, 
                   return_train_score=True,
                   n_jobs=-1)