Train/test-split + nauwkeurigheid berekenen

Tijd om te oefenen met het splitsen van je gegevens in train- en testsets met de churn_df-gegevensset!

Er zijn NumPy-arrays voor je aangemaakt met de features als X en de doelvariabele als y.

Deze oefening maakt deel uit van de cursus

Supervised Learning met scikit-learn

Cursus bekijken

Oefeninstructies

Importeer train_test_split uit sklearn.model_selection.
Split X en y in train- en testsets, stel test_size in op 20%, random_state op 42, en zorg dat de verhoudingen van de doellabels overeenkomen met die van de oorspronkelijke gegevensset.
Fit het knn-model op de trainingsgegevens.
Bereken en print de nauwkeurigheid van het model op de testgegevens.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import the module
from ____ import ____

X = churn_df.drop("churn", axis=1).values
y = churn_df["churn"].values

# Split into training and test sets
X_train, X_test, y_train, y_test = ____(____, ____, test_size=____, random_state=____, stratify=____)
knn = KNeighborsClassifier(n_neighbors=5)

# Fit the classifier to the training data
____

# Print the accuracy
print(knn.score(____, ____))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Supervised Learning met scikit-learn

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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 Exercise 7: Modelprestaties meten Exercise 8: Train/test-split + nauwkeurigheid berekenen

Huidige oefening

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