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Course Content

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 ValuesImputing ChallengeOrdinalEncoderOne-Hot EncoderLabelEncoderEncoding ChallengeWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScaling ChallengePreprocessing Summary

3. Pipelines

What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorFinal Estimator Challenge

4. Modeling

ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationCross-Validation ChallengeGridSearchCVThe Flaw of GridSearchCVRandomizedSearchCV. Hyperparameter Tuning ChallengeModeling SummaryPutting It All Together

ML Introduction with scikit-learn

Final Estimator ChallengeFinal Estimator Challenge

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe object.

Everything was clear?

Section 3. Chapter 6
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