Prediksi klasifikasi

Dalam validasi model, sering kali penting untuk mengetahui lebih dari sekadar klasifikasi akhir. Saat memprediksi siapa yang akan menang dalam sebuah pertandingan, kebanyakan orang juga tertarik pada seberapa besar kemungkinan sebuah tim akan menang.

Probability	Prediction	Meaning
0 < .50	0	Tim Kalah
.50 +	1	Tim Menang

Dalam latihan ini, Anda akan melihat metode .predict() dan .predict_proba() menggunakan himpunan data tic_tac_toe. Metode pertama akan memberikan prediksi apakah Pemain Satu akan memenangkan permainan, dan metode kedua akan memberikan probabilitas Pemain Satu untuk menang. Gunakan rfc sebagai model klasifikasi random forest.

Latihan ini adalah bagian dari kursus

Validasi Model di Python

Petunjuk latihan

Buat dua array prediksi: satu untuk nilai klasifikasi dan satu lagi untuk probabilitas terprediksi.
Gunakan metode .value_counts() untuk pandas Series guna mencetak jumlah observasi yang ditetapkan ke setiap kelas.
Cetak observasi pertama dari probability_predictions untuk melihat bagaimana probabilitas disusun.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Fit the rfc model. 
rfc.fit(X_train, y_train)

# Create arrays of predictions
classification_predictions = rfc.____(X_test)
probability_predictions = rfc.____(X_test)

# Print out count of binary predictions
print(pd.Series(____).____())

# Print the first value from probability_predictions
print('The first predicted probabilities are: {}'.format(____[____]))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Validasi Model di Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters.

Exercise 1: Pengantar validasi model Exercise 2: Langkah-langkah pemodelan Exercise 3: Data yang pernah vs. belum pernah dilihat Exercise 4: Model regresi Exercise 5: Atur parameter dan latih model Exercise 6: Tingkat kepentingan fitur Exercise 7: Model klasifikasi Exercise 8: Prediksi klasifikasi

Latihan Saat Ini

Exercise 9: Menggunakan kembali parameter model Exercise 10: Klasifikator random forest

This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three.

Exercise 1: Creating train, test, and validation datasets Exercise 2: Create one holdout set Exercise 3: Create two holdout sets Exercise 4: Why use holdout sets Exercise 5: Accuracy metrics: regression models Exercise 6: Mean absolute error Exercise 7: Mean squared error Exercise 8: Performance on data subsets Exercise 9: Classification metrics Exercise 10: Confusion matrices Exercise 11: Confusion matrices, again Exercise 12: Precision vs. recall Exercise 13: The bias-variance tradeoff Exercise 14: Error due to under/over-fitting Exercise 15: Am I underfitting?

Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance.

Exercise 1: The problems with holdout sets Exercise 2: Two samples Exercise 3: Potential problems Exercise 4: Cross-validation Exercise 5: scikit-learn's KFold()Exercise 6: Using KFold indices Exercise 7: sklearn's cross_val_score()Exercise 8: scikit-learn's methods Exercise 9: Implement cross_val_score()Exercise 10: Leave-one-out-cross-validation (LOOCV)Exercise 11: When to use LOOCV Exercise 12: Leave-one-out-cross-validation

The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model.

Exercise 1: Introduction to hyperparameter tuning Exercise 2: Creating Hyperparameters Exercise 3: Running a model using ranges Exercise 4: RandomizedSearchCV Exercise 5: Preparing for RandomizedSearch Exercise 6: Implementing RandomizedSearchCV Exercise 7: Selecting your final model Exercise 8: Best classification accuracy Exercise 9: Selecting the best precision model Exercise 10: Course completed!