Using sklearn's GridSearchCV with Pipeline for Hyperparameter Tuning in Machine Learning
Автор: Dr. Data Science
Загружено: 2022-07-26
Просмотров: 8307
In this video, we discuss how to perform hyperparameter tuning for machine learning and deep learning problems in a simple and efficient way. The idea behind sklearn's pipeline is to apply a sequence of transformers/preprocessing techniques in a sequential way and then apply an estimator (regressor or classifier). On the other hand, GridSearchCV can be used to perform an exhaustive search over specified parameter values for an estimator or a pipeline. We show that parameters of pipelines can be set using ‘__’ separated parameter names to perform hyperparameter optimization (param_grid). We use a polynomial regression model in Python to demonstrate the combination of Pipeline and GridSearchCV. These are the main modules that we imported:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Ridge
#machinelearning #sklearn #regularization
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