Visualizzazione della complessità del modello

Ora che hai calcolato l'accuratezza del modello KNN sui set di allenamento e di test utilizzando vari valori di n_neighbors, puoi creare una curva di complessità del modello per visualizzare come cambiano le prestazioni quando il modello diventa meno complesso!

Le variabili neighbors, train_accuracies e test_accuracies, generate nell'esercizio precedente, sono state precaricate. Traccerai i risultati per trovare il numero ottimale di vicini per il tuo modello.

Questo esercizio fa parte del corso

Apprendimento supervisionato con scikit-learn

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Istruzioni dell'esercizio

Aggiungi un titolo "KNN: Varying Number of Neighbors".
Traccia il metodo .values() di train_accuracies sull'asse delle ordinate rispetto a neighbors sull'asse delle ascisse, con l'etichetta "Training Accuracy".
Traccia il metodo .values() di test_accuracies sull'asse delle ordinate rispetto a neighbors sull'asse delle ascisse, con l'etichetta "Testing Accuracy".
Visualizza il grafico.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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
____

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Apprendimento supervisionato con scikit-learn

IntermediárioNível de habilidade

4.8+

Inizia il corso 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: Apprendimento automatico con scikit-learn Exercise 2: Classificazione binaria Exercise 3: Il flusso di lavoro dell'apprendimento supervisionato Exercise 4: La sfida della classificazione Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Prevedere Exercise 7: Misurare le prestazioni del modello Exercise 8: Divisione treno/test + calcolo dell'accuratezza Exercise 9: Overfitting e underfitting Exercise 10: Visualizzazione della complessità del modello

Esercizio in corso

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