After training a machine learning model, we often need to save the model so that later we can load the model and use it without training it again. We can use the Python module pickle for that purpose. Let’s say we are reading the Pima Indians Diabetes dataset. The...
In randomized search parameter tuning, a randomized search is performed on hyperparameters. Hyperparameters are parameters that are passed as arguments to the constructor of the estimator. For example, in Lasso regression, the parameter alpha can be considered as a...
Hyperparameters are parameters that are passed as arguments to the constructor of the estimator. For example, in Lasso regression, the parameter alpha can be considered as a hyperparameter. We can tune these hyperparameters to optimize the performance of our model. In...
Let’s say we want to solve a classification problem. Now, we can use logistic regression, KNN classifier, decision tree classifier, Gaussian Naive Bayes classifier, Support Vector Machine classifier, etc., to solve the classification problem. We can also create a...
Gradient boosting is an ensemble technique using which multiple weak learners can be ensembled to create a stronger prediction model. These weak learners are decision trees. And these decision trees are used sequentially so that one decision tree can be built based on...
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