Challenge: Solving Task Using Bagging Classifier
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The load_breast_cancer
dataset is a built-in dataset provided by scikit-learn. It is commonly used for binary classification tasks, particularly in the context of breast cancer diagnosis. This dataset contains features that are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The aim is to predict whether a given mass is malignant (cancerous) or benign (non-cancerous).
Your task is to solve the classification problem using BaggingClassifier
on load_breast_cancer
dataset:
- Create an instance of
BaggingClassifier
class: specify base SVC (Support Vector Classifier) model and set the number of base estimators equal to10
. - Fit the ensemble model.
- Get the final result using soft voting technique: for each sample in test dataset get the probability matrix and find the class with maximum probability.
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Challenge: Solving Task Using Bagging Classifier
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Swipe to start coding
The load_breast_cancer
dataset is a built-in dataset provided by scikit-learn. It is commonly used for binary classification tasks, particularly in the context of breast cancer diagnosis. This dataset contains features that are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The aim is to predict whether a given mass is malignant (cancerous) or benign (non-cancerous).
Your task is to solve the classification problem using BaggingClassifier
on load_breast_cancer
dataset:
- Create an instance of
BaggingClassifier
class: specify base SVC (Support Vector Classifier) model and set the number of base estimators equal to10
. - Fit the ensemble model.
- Get the final result using soft voting technique: for each sample in test dataset get the probability matrix and find the class with maximum probability.
Løsning
Takk for tilbakemeldingene dine!
Awesome!
Completion rate improved to 4.55single