Memvisualisasikan kompleksitas model

Sekarang setelah Anda menghitung akurasi model KNN pada himpunan data pelatihan dan pengujian menggunakan berbagai nilai n_neighbors, Anda dapat membuat kurva kompleksitas model untuk memvisualisasikan bagaimana kinerja berubah saat model menjadi kurang kompleks!

Variabel neighbors, train_accuracies, dan test_accuracies, yang Anda hasilkan pada latihan sebelumnya, telah dimuat untuk Anda. Anda akan memplot hasilnya untuk membantu menemukan jumlah tetangga yang optimal bagi model Anda.

Latihan ini adalah bagian dari kursus

Supervised Learning dengan scikit-learn

Petunjuk latihan

Tambahkan judul "KNN: Varying Number of Neighbors".
Plot metode .values() dari train_accuracies pada sumbu y terhadap neighbors pada sumbu x, dengan label "Training Accuracy".
Plot metode .values() dari test_accuracies pada sumbu y terhadap neighbors pada sumbu x, dengan label "Testing Accuracy".
Tampilkan plot.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Add a title
plt.title("____")

# Plot training accuracies
plt.plot(____, ____, label="____")

# Plot test accuracies
plt.plot(____, ____, label="____")

plt.legend()
plt.xlabel("Number of Neighbors")
plt.ylabel("Accuracy")

# Display the plot
____

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

Latihan Saat Ini

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