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ML Introduction with scikit-learn

Evaluate the Model with Cross-ValidationEvaluate 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

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.

Tudo estava claro?

Seção 4. Capítulo 5
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course content

Conteúdo do Curso

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 ValuesImpute Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderEncode Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScale the FeaturesPreprocessing Summary

3. Pipelines

What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorCreate 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

ML Introduction with scikit-learn

Evaluate the Model with Cross-ValidationEvaluate 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

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.

Tudo estava claro?

Seção 4. Capítulo 5
toggle bottom row
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