Cross-validation untuk R-squared

Cross-validation adalah pendekatan penting untuk mengevaluasi model. Metode ini memaksimalkan jumlah data yang tersedia bagi model, karena model tidak hanya dilatih tetapi juga diuji pada seluruh data yang tersedia.

Dalam latihan ini, Anda akan membangun model regresi linear, lalu menggunakan cross-validation 6-fold untuk menilai akurasinya dalam memprediksi penjualan berdasarkan pengeluaran iklan media sosial. Anda akan menampilkan skor individu untuk masing-masing dari enam fold.

Himpunan data sales_df telah dipisahkan menjadi y untuk variabel target dan X untuk fitur, dan sudah dimuat untuk Anda. LinearRegression telah diimpor dari sklearn.linear_model.

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

Petunjuk latihan

Impor KFold dan cross_val_score.
Buat kf dengan memanggil KFold(), tetapkan jumlah pembagian menjadi enam, shuffle ke True, dan atur seed sebesar 5.
Lakukan cross-validation menggunakan reg pada X dan y, teruskan kf ke cv.
Cetak cv_scores.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import the necessary modules
from ____.____ import ____, ____

# Create a KFold object
kf = ____(n_splits=____, shuffle=____, random_state=____)

reg = LinearRegression()

# Compute 6-fold cross-validation scores
cv_scores = ____(____, ____, ____, cv=____)

# Print scores
print(____)

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 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: Pengantar regresi Exercise 2: Membuat fitur Exercise 3: Membangun model regresi linear Exercise 4: Memvisualisasikan model regresi linear Exercise 5: Dasar-dasar regresi linear Exercise 6: Fit dan prediksi untuk regresi Exercise 7: Kinerja regresi Exercise 8: Validasi silang Exercise 9: Cross-validation untuk R-squared

Latihan Saat Ini

Exercise 10: Menganalisis metrik cross-validation Exercise 11: Regresi teratur Exercise 12: Regularized regression: Ridge Exercise 13: Regresi lasso untuk pentingnya fitur

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