Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Apprendre Challenge: Tuning Hyperparameters with RandomizedSearchCV | Modeling
ML Introduction with scikit-learn
course content

Contenu du cours

ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow
2. Preprocessing Data with Scikit-learn
Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesChallenge: Imputing Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderChallenge: Encoding Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerChallenge: Scaling the Features
3. Pipelines
What is PipelineColumnTransformerEfficient Data Preprocessing with PipelinesChallenge: Creating a PipelineFinal EstimatorChallenge: Creating a Complete ML Pipeline
4. Modeling
ModelsKNeighborsClassifierEvaluating the ModelCross-ValidationChallenge: Evaluating the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVChallenge: Tuning Hyperparameters with RandomizedSearchCVModeling SummaryChallenge: Putting It All Together

book
Challenge: Tuning Hyperparameters with RandomizedSearchCV

The idea behind RandomizedSearchCV is that it works the same as GridSearchCV, but instead of trying all the combinations, it tries a randomly sampled subset.

For example, this param_grid will have 100 combinations:

The GridSearchCV would try all of them, which is time-consuming. With RandomizedSearchCV, you can try only a randomly chosen subset of, say, 20 combinations. It usually leads to a little worse result, but works much faster.

You can control the number of combinations to be tested using the n_iter argument (set to 10 by default). Apart from that, working with it is the same as with GridSearchCV.

Tâche

Swipe to start coding

Your task is to build GridSearchCV and RandomizedSearchCV with 20 combinations and compare the results.

  1. Initialize the RandomizedSearchCV object. Pass the parameters grid and set the number of combinations to 20.
  2. Initialize the GridSearchCV object.
  3. Train both GridSearchCV and RandomizedSearchCV objects.
  4. Print the best estimator of grid.
  5. Print the best score of randomized.

Solution

Switch to desktopPassez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous
Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 4. Chapitre 8
toggle bottom row

book
Challenge: Tuning Hyperparameters with RandomizedSearchCV

The idea behind RandomizedSearchCV is that it works the same as GridSearchCV, but instead of trying all the combinations, it tries a randomly sampled subset.

For example, this param_grid will have 100 combinations:

The GridSearchCV would try all of them, which is time-consuming. With RandomizedSearchCV, you can try only a randomly chosen subset of, say, 20 combinations. It usually leads to a little worse result, but works much faster.

You can control the number of combinations to be tested using the n_iter argument (set to 10 by default). Apart from that, working with it is the same as with GridSearchCV.

Tâche

Swipe to start coding

Your task is to build GridSearchCV and RandomizedSearchCV with 20 combinations and compare the results.

  1. Initialize the RandomizedSearchCV object. Pass the parameters grid and set the number of combinations to 20.
  2. Initialize the GridSearchCV object.
  3. Train both GridSearchCV and RandomizedSearchCV objects.
  4. Print the best estimator of grid.
  5. Print the best score of randomized.

Solution

Switch to desktopPassez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous
Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 4. Chapitre 8
Switch to desktopPassez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous
We're sorry to hear that something went wrong. What happened?
some-alt