GridSearchCV | Grid Search - Hyper Parameter Tuning | Scikit Learn Tutorial | Intellipaat
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In this video, we will focus on one of the hyperparameter tuning techniques known as “Grid Search”. We will begin by performing feature engineering using pandas and then proceed to model training using the scikit-learn library. After that, we will choose a machine-learning model and we will optimize that machine-learning model by testing different combinations or performing hyperparameter tuning using GridSearchCV.
🔵 Following topics are covered in this session:
00:00 - Introduction
00:30 - Feature Engineering
08:18 - Model Training
12:11 - What are Hyperparameters?
13:44 - What is Grid Search?
15:00 - How does Grid Search Work?
16:24 - Hyperparameter tuning using GridSearchCV
✅ What is a GridSearchCV?
GridSearchCV is a method for finding the optimal combination of hyperparameters for a machine-learning model. It systematically tries out different combinations of hyperparameter values and selects the one that produces the best performance on the training data.
✅ What is hyperparameter tuning in ML?
Hyperparameter tuning is like fine-tuning a machine learning model's knobs and dials to get the best results. It involves adjusting settings that control the model's learning process, such as the number of training examples or the complexity of the model.
✅ What are the methods of hyperparameter tuning?
There are three methods for hyperparameter tuning: grid search, random search, and Bayesian optimization. Grid Search CV meticulously evaluates all combinations of hyperparameter values, Random Search CV randomly samples combinations, Bayesian Optimization guides the search probabilistically, and Manual Search requires manual experimentation.
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