k-Nearest Neighbors: voorspellen

Nu je een KNN-classifier hebt getraind, kun je die gebruiken om het label van nieuwe datapunten te voorspellen. Alle beschikbare data is gebruikt voor training, maar gelukkig zijn er nieuwe observaties beschikbaar. Deze zijn voor je ingeladen als X_new.

Het model knn, dat je in de vorige oefening hebt gemaakt en getraind, is voor je ingeladen. Je gebruikt je classifier om de labels van een set nieuwe datapunten te voorspellen:

X_new = np.array([[30.0, 17.5],
                  [107.0, 24.1],
                  [213.0, 10.9]])

Deze oefening maakt deel uit van de cursus

Supervised Learning met scikit-learn

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Oefeninstructies

Maak y_pred door de doelwaarden van de onbekende features X_new te voorspellen met het knn-model.
Print de voorspelde labels voor de set voorspellingen.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Predict the labels for the X_new
y_pred = ____

# Print the predictions
print("Predictions: {}".format(____))

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 met scikit-learn Exercise 2: Binaire classificatie Exercise 3: De supervised learning-workflow Exercise 4: De classificatie-uitdaging Exercise 5: k-Nearest Neighbors: Fitten Exercise 6: k-Nearest Neighbors: voorspellen

Huidige oefening

Exercise 7: Modelprestaties meten Exercise 8: Train/test-split + nauwkeurigheid berekenen Exercise 9: Overfitting en underfitting Exercise 10: Modelcomplexiteit visualiseren

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: Preprocessing data Exercise 2: Creating dummy variables Exercise 3: Regression with categorical features Exercise 4: Handling missing data Exercise 5: Dropping missing data Exercise 6: Pipeline for song genre prediction: I Exercise 7: Pipeline for song genre prediction: II Exercise 8: Centering and scaling Exercise 9: Centering and scaling for regression Exercise 10: Centering and scaling for classification Exercise 11: Evaluating multiple models Exercise 12: Visualizing regression model performance Exercise 13: Predicting on the test set Exercise 14: Visualizing classification model performance Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations