Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Evaluate the Model with Cross-Validation | Modeling
ML Introduction with scikit-learn
course content

Conteúdo do Curso

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 FeaturesPreprocessing Summary
3. Pipelines
What is PipelineColumnTransformerEfficient Data Preprocessing with PipelinesChallenge: Creating a PipelineFinal EstimatorChallenge: Creating a Complete ML Pipeline
4. Modeling
ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationEvaluate the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVTune Hyperparameters with RandomizedSearchCVModeling SummaryPutting It All Together

bookEvaluate the Model with Cross-Validation

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation.
The data is an already preprocessed Penguins dataset.
Some functions you will use:

Tarefa
test

Swipe to show code editor

Build a 4-nearest neighbors classifier and evaluate its performance using the cross-validation score first, then split the data into train-test sets, train the model using the training set, and evaluate it using the test set.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. Calculate the cross-validation scores of this model with the number of folds set to 3.
    Note: you can pass an untrained model to a cross_val_score() function.
  3. Use a suitable function to split X, y.
  4. Train the model using the training set.
  5. Evaluate the model using the test set.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 4. Capítulo 5
toggle bottom row

bookEvaluate the Model with Cross-Validation

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation.
The data is an already preprocessed Penguins dataset.
Some functions you will use:

Tarefa
test

Swipe to show code editor

Build a 4-nearest neighbors classifier and evaluate its performance using the cross-validation score first, then split the data into train-test sets, train the model using the training set, and evaluate it using the test set.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. Calculate the cross-validation scores of this model with the number of folds set to 3.
    Note: you can pass an untrained model to a cross_val_score() function.
  3. Use a suitable function to split X, y.
  4. Train the model using the training set.
  5. Evaluate the model using the test set.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 4. Capítulo 5
toggle bottom row

bookEvaluate the Model with Cross-Validation

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation.
The data is an already preprocessed Penguins dataset.
Some functions you will use:

Tarefa
test

Swipe to show code editor

Build a 4-nearest neighbors classifier and evaluate its performance using the cross-validation score first, then split the data into train-test sets, train the model using the training set, and evaluate it using the test set.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. Calculate the cross-validation scores of this model with the number of folds set to 3.
    Note: you can pass an untrained model to a cross_val_score() function.
  3. Use a suitable function to split X, y.
  4. Train the model using the training set.
  5. Evaluate the model using the test set.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation.
The data is an already preprocessed Penguins dataset.
Some functions you will use:

Tarefa
test

Swipe to show code editor

Build a 4-nearest neighbors classifier and evaluate its performance using the cross-validation score first, then split the data into train-test sets, train the model using the training set, and evaluate it using the test set.

  1. Initialize a KNeighborsClassifier with 4 neighbors.
  2. Calculate the cross-validation scores of this model with the number of folds set to 3.
    Note: you can pass an untrained model to a cross_val_score() function.
  3. Use a suitable function to split X, y.
  4. Train the model using the training set.
  5. Evaluate the model using the test set.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 4. Capítulo 5
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
We're sorry to hear that something went wrong. What happened?
some-alt