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ML Introduction with scikit-learn
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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 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

book
Evaluate 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:

Aufgabe

Swipe to start coding

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.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 5
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book
Evaluate 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:

Aufgabe

Swipe to start coding

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.

Lösung

Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 5
Switch to desktopWechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden
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