Pembagian train/test + menghitung akurasi

Sekarang saatnya berlatih membagi data Anda menjadi himpunan pelatihan dan pengujian dengan himpunan data churn_df!

Array NumPy telah disiapkan untuk Anda yang berisi fitur sebagai X dan variabel target sebagai y.

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

Petunjuk latihan

Impor train_test_split dari sklearn.model_selection.
Bagi X dan y menjadi himpunan pelatihan dan pengujian, tetapkan test_size sebesar 20%, random_state ke 42, dan pastikan proporsi label target mencerminkan himpunan data asli.
Latih model knn pada data pelatihan.
Hitung dan cetak akurasi model untuk data pengujian.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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(____, ____))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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 dengan scikit-learn Exercise 2: Klasifikasi biner Exercise 3: Alur kerja supervised learning Exercise 4: Tantangan klasifikasi Exercise 5: k-Nearest Neighbors: Pemodelan (Fit)Exercise 6: k-Nearest Neighbors: Prediksi Exercise 7: Mengukur kinerja model Exercise 8: Pembagian train/test + menghitung akurasi

Latihan Saat Ini

Exercise 9: Overfitting dan underfitting Exercise 10: Memvisualisasikan kompleksitas model

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