Hyperparameter Tuning for a Random Forest Classifier
Hyperparameter Tuning for a Random Forest Classifier Data Science Project
Classification in Depth with Scikit-Learn

Hyperparameter Tuning for a Random Forest Classifier

The project will cover topics such as understanding hyperparameters, the impact they have on model performance, and how to tune them to achieve the best results. Using a Random Forest model you will learn how to tune hyperparameters. We will be using the Ghouls, Goblins, and Ghosts dataset, so let's have fun an tune the model.
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Hyperparameter Tuning for a Random Forest ClassifierHyperparameter Tuning for a Random Forest Classifier
Project Created by

Verónica Barraza

Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

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Based on this plot and a correlation analysis, which variable present the hightest asociation between them

paiplot

codevalidated

Train and test split

Use `train_test_split√ to split the data into training and testing sets. Split the dataset in 80% training, 20% testing and random_state=0.

Store the values in the variables in X_train,X_test,y_train, y_test,random_state .

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Which value has the Best hyperparameters of max_depth?

multiplechoice

Use GridSearchCV to search over a range of values for max_depth (from 1 to 20) and n_estimators (from 1 to 10) hyperparameters to find the combination that yields the best performance.

For this task use cv=5, and random_state=42 and compute the Best mean score

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True or False: For this example, the best hyperparameter obtained is max_depth = 19

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The best hyperparameters for a given machine learning algorithm will always depend on the specific dataset and problem being addressed.

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If searching among a large number of hyperparameters, you should try values in a grid rather than random values, so that you can carry out the search more systematically and not rely on chance.

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Underfitting can occur when hyperparameters are tuned too much on a small dataset, leading to poor generalization performance on new data.

Hyperparameter Tuning for a Random Forest ClassifierHyperparameter Tuning for a Random Forest Classifier
Project Created by

Verónica Barraza

This project is part of

Classification in Depth with Scikit-Learn

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