Voorspellen op de testset

In de vorige oefening leken lineaire regressie en ridge vergelijkbare resultaten op te leveren. Het is prima om een van deze modellen te kiezen; je kunt echter de voorspellende prestaties op de testset controleren om te zien of een van beide het beter doet dan de ander.

Je gebruikt root mean squared error (RMSE) als metriek. De dictionary models, met de namen en instanties van de twee modellen, is alvast voor je ingeladen, samen met de train- en doelarrays X_train_scaled, X_test_scaled, y_train en y_test.

Deze oefening maakt deel uit van de cursus

Supervised Learning met scikit-learn

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Oefeninstructies

Importeer root_mean_squared_error.
Fit het model op de geschaalde trainingsfeatures en de trainingslabels.
Maak voorspellingen met de geschaalde testfeatures.
Bereken de RMSE door de testsetlabels en de voorspelde labels door te geven.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import root_mean_squared_error
from ____.____ import ____

for name, model in models.items():
  # Fit the model to the training data
  ____
  
  # Make predictions on the test set
  y_pred = ____
  
  # Calculate the test_rmse
  test_rmse = ____(____, ____)
  print("{} Test Set RMSE: {}".format(name, test_rmse))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Supervised Learning met scikit-learn

SkillTag.level.intermediateSkillTag.label

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In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy Exercise 9: Overfitting and underfitting Exercise 10: Visualizing model complexity

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduction to regression Exercise 2: Creating features Exercise 3: Building a linear regression model Exercise 4: Visualizing a linear regression model Exercise 5: The basics of linear regression Exercise 6: Fit and predict for regression Exercise 7: Regression performance Exercise 8: Cross-validation Exercise 9: Cross-validation for R-squared Exercise 10: Analyzing cross-validation metrics Exercise 11: Regularized regression Exercise 12: Regularized regression: Ridge Exercise 13: Lasso regression for feature importance

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Exercise 1: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Exercise 1: Gegevens preprocessen Exercise 2: Dummyvariabelen maken Exercise 3: Regressie met categorische features Exercise 4: Omgaan met missende gegevens Exercise 5: Missende gegevens verwijderen Exercise 6: Pipeline voor het voorspellen van muziekgenre: I Exercise 7: Pipeline voor het voorspellen van muziekgenres: II Exercise 8: Centreren en schalen Exercise 9: Centreren en schalen voor regressie Exercise 10: Centreren en schalen voor classificatie Exercise 11: Meerdere modellen evalueren Exercise 12: Prestatie van regressiemodellen visualiseren Exercise 13: Voorspellen op de testset

Huidige oefening

Exercise 14: Classificatiemodelprestaties visualiseren Exercise 15: Pipeline voor het voorspellen van songpopulariteit Exercise 16: Gefeliciteerd