Regresi dengan fitur kategorikal

Sekarang Anda telah membuat music_dummies yang berisi fitur biner untuk setiap genre lagu, saatnya membangun model ridge regression untuk memprediksi popularitas lagu.

music_dummies telah dimuat untuk Anda, bersama dengan Ridge, cross_val_score, numpy sebagai np, dan objek KFold yang disimpan sebagai kf.

Model akan dievaluasi dengan menghitung rata-rata RMSE, tetapi terlebih dahulu, Anda perlu mengubah skor untuk setiap lipatan menjadi nilai positif dan mengambil akar kuadratnya. Metrik ini menunjukkan rata-rata galat dari prediksi model kita, sehingga dapat dibandingkan dengan simpangan baku nilai target—"popularity".

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

Petunjuk latihan

Buat X yang berisi semua fitur dalam music_dummies, dan y yang berupa kolom "popularity".
Instansiasi model ridge regression dengan menetapkan alpha sama dengan 0.2.
Lakukan cross-validation pada X dan y menggunakan model ridge, tetapkan cv sama dengan kf, dan gunakan negative mean squared error sebagai metrik penilaian.
Cetak nilai RMSE dengan mengonversi scores negatif menjadi positif dan mengambil akar kuadratnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create X and y
X = ____
y = ____

# Instantiate a ridge model
ridge = ____

# Perform cross-validation
scores = ____(____, ____, ____, cv=____, scoring="____")

# Calculate RMSE
rmse = np.____(____)
print("Average RMSE: {}".format(np.mean(rmse)))
print("Standard Deviation of the target array: {}".format(np.std(y)))

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: 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: Praproses data Exercise 2: Membuat variabel dummy Exercise 3: Regresi dengan fitur kategorikal

Latihan Saat Ini

Exercise 4: Menangani data hilang Exercise 5: Menghapus data hilang Exercise 6: Pipeline untuk prediksi genre lagu: I Exercise 7: Pipeline untuk prediksi genre lagu: II Exercise 8: Pemusatan dan penskalaan Exercise 9: Pemusatan dan penskalaan untuk regresi Exercise 10: Pemusatan dan penskalaan untuk klasifikasi Exercise 11: Mengevaluasi beberapa model Exercise 12: Memvisualisasikan kinerja model regresi Exercise 13: Memprediksi pada himpunan uji Exercise 14: Memvisualisasikan kinerja model klasifikasi Exercise 15: Pipeline untuk memprediksi popularitas lagu Exercise 16: Selamat