Tuning the Hyperparameters of your Machine Learning Model using GridSearchCV
Briefly

from towardsdatascience.com3 years ago
Fine-tuning hyperparameters can be done easily using the GridSearchCV function in the sklearn module.
towardsdatascience.comhttps://towardsdatascience.com/tuning-the-hyperparameters-of-your-machine-learning-model-using-gridsearchcv-7fc2bb76ff27
For classification problems, algorithms like Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Naive Bayes can be used.
towardsdatascience.comhttps://towardsdatascience.com/tuning-the-hyperparameters-of-your-machine-learning-model-using-gridsearchcv-7fc2bb76ff27
Feature selection is important to remove collinear and non-correlated columns before training the model.
towardsdatascience.comhttps://towardsdatascience.com/tuning-the-hyperparameters-of-your-machine-learning-model-using-gridsearchcv-7fc2bb76ff27
It is essential to split the dataset into training and testing sets, and standardize the data before applying a machine learning algorithm.
towardsdatascience.comhttps://towardsdatascience.com/tuning-the-hyperparameters-of-your-machine-learning-model-using-gridsearchcv-7fc2bb76ff27
Read at towardsdatascience.com
#machine-learning #hyperparameter-optimization #classification #logistic-regression #data-preprocessing
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