Conteúdo do Curso
Ensemble Learning
Ensemble Learning
Challenge: Solving Task Using AdaBoost Classifier
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The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.
Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:
- Split data into train and test sets.
- Use the
AdaBoostClassifier()constructor to create the model with 50 base estimators.
Note
If we don't specify the base model of
AdaBoostClassifer, the Decision Tree Classifier will be used by default.
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Challenge: Solving Task Using AdaBoost Classifier
Swipe to show code editor
The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.
Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:
- Split data into train and test sets.
- Use the
AdaBoostClassifier()constructor to create the model with 50 base estimators.
Note
If we don't specify the base model of
AdaBoostClassifer, the Decision Tree Classifier will be used by default.
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu feedback!
Challenge: Solving Task Using AdaBoost Classifier
Swipe to show code editor
The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.
Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:
- Split data into train and test sets.
- Use the
AdaBoostClassifier()constructor to create the model with 50 base estimators.
Note
If we don't specify the base model of
AdaBoostClassifer, the Decision Tree Classifier will be used by default.
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu feedback!
Swipe to show code editor
The load_wine dataset is a classic example used for classification tasks. It consists of 178 samples, each representing a different wine cultivar. The dataset comprises 13 numerical attributes that describe various chemical characteristics of the wines, including features like alcohol content, malic acid concentration, and ash content. The target variable consists of three distinct classes representing the three different cultivars.
Your task is to use AdaBoost Classifier to solve the classification problem on the load_wine dataset:
- Split data into train and test sets.
- Use the
AdaBoostClassifier()constructor to create the model with 50 base estimators.
Note
If we don't specify the base model of
AdaBoostClassifer, the Decision Tree Classifier will be used by default.
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo