In scikit-learn’s GridSearchCV (Grid Search Cross Validation), the parameter cv stands for “cross-validation.” It determines the cross-validation splitting strategy to be used when evaluating the performance of a machine learning model. When cv is set to an integer (e.g., cv=5), it represents the number of folds in a (Stratified) K-Fold cross-validation. For example, cv=5 means…
np.argsort is a NumPy function that returns the indices that would sort an array along a specified axis. It performs an indirect sort on the input array and returns an array of indices that represent the sorted order of the elements. The returned indices can be used to construct a sorted version of the input…
Now put the printed best_model here below and run:
Grid search is a tuning technique that attempts to compute the optimum values of hyperparameters. It is an exhaustive search that is performed on the specific parameter values of a model. The parameters of the estimator/model used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
In general, the deeper you allow your tree to grow, the more complex your model will become because you will have more splits and it captures more information about the data and this is one of the root causes of overfitting. We can limit the tree with max_depth of tree:
In scikit-learn, the feature_importances_ attribute is associated with tree-based models, such as Decision Trees, Random Forests, and Gradient Boosted Trees. This attribute provides a way to assess the importance of each feature (or variable) in making predictions with the trained model. When you train a tree-based model, the algorithm makes decisions at each node based…
To visualize a decision tree in scikit-learn, you can use the plot_tree function from the sklearn.tree module. This function allows you to generate a visual representation of the decision tree. Here’s a simple example: To show the decision tree as text in scikit-learn, you can use the export_text function from the sklearn.tree module. This function…
For building a decision tree model in scikit-learn (sklearn), you need to import the relevant modules and classes. Here are the main components you’ll typically use:
To import a function from a separate Jupyter Notebook (.ipynb file) into another notebook, you can use the %run magic command:
To grab random sample from a dataset in Python, you can use the pandas library. Assuming your dataset is stored in a pandas DataFrame, you can use the sample method to randomly select rows. Here’s an example: In this example, n=5 specifies the number of rows to sample, and random_state is set to ensure reproducibility.
