Pipeline untuk memprediksi popularitas lagu

Untuk latihan terakhir, Anda akan membangun pipeline untuk imputasi nilai hilang, penskalaan fitur, dan melakukan penyetelan hyperparameter pada model logistic regression. Tujuannya adalah menemukan parameter dan akurasi terbaik saat memprediksi genre lagu!

Semua model dan objek yang diperlukan untuk membangun pipeline telah dimuatkan sebelumnya untuk Anda.

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

Petunjuk latihan

Buat langkah-langkah untuk pipeline dengan memanggil simple imputer, standard scaler, dan model logistic regression.
Buat objek pipeline, lalu teruskan variabel steps.
Instansiasi objek grid search untuk melakukan cross-validation menggunakan pipeline dan parameter.
Cetak parameter terbaik dan hitung serta cetak skor akurasi himpunan uji untuk objek grid search.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create steps
steps = [("imp_mean", ____()), 
         ("scaler", ____()), 
         ("logreg", ____())]

# Set up pipeline
pipeline = ____(____)
params = {"logreg__solver": ["newton-cg", "saga", "lbfgs"],
         "logreg__C": np.linspace(0.001, 1.0, 10)}

# Create the GridSearchCV object
tuning = ____(____, param_grid=____)
tuning.fit(X_train, y_train)
y_pred = tuning.predict(X_test)

# Compute and print performance
print("Tuned Logistic Regression Parameters: {}, Accuracy: {}".format(____.____, ____.____))

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 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

Latihan Saat Ini

Exercise 16: Selamat