Duas amostras

Depois de construir vários modelos de classificação com base no conjunto de dados tic_tac_toe, você percebe que alguns modelos não generalizam tão bem quanto outros. Você criou divisões de treino e teste como aprendeu, então fica curioso por que seu processo de validação não está funcionando.

Após tentar uma divisão diferente entre treino e teste, você notou acurácias diferentes para seu modelo de Machine Learning. Antes de se frustrar com os resultados variáveis, você decidiu investigar o que mais pode estar acontecendo.

Este exercicio faz parte do curso

Validação de Modelos em Python

Ver curso

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

# Create two different samples of 200 observations 
sample1 = tic_tac_toe.____(____, random_state=1111)
sample2 = tic_tac_toe.____(____, random_state=1171)

Editar e Executar Código

Este exercicio faz parte do curso

Validação de Modelos em Python

IntermediárioNível de habilidade

4.9+

840 reviews

Comece o curso gratuitamente

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: Introduction to model validation Exercise 2: Modeling steps Exercise 3: Seen vs. unseen data Exercise 4: Regression models Exercise 5: Set parameters and fit a model Exercise 6: Feature importances Exercise 7: Classification models Exercise 8: Classification predictions Exercise 9: Reusing model parameters Exercise 10: Random forest classifier

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: Os problemas dos conjuntos de holdout Exercise 2: Duas amostras

Exercicio Atual

Exercise 3: Problemas potenciais Exercise 4: Validação cruzada Exercise 5: KFold() do scikit-learn Exercise 6: Usando índices do KFold Exercise 7: cross_val_score() do sklearn Exercise 8: Métodos do scikit-learn Exercise 9: Implementar cross_val_score()Exercise 10: Leave-one-out cross-validation (LOOCV)Exercise 11: Quando usar 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!