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Challenge: Solving Task Using Regularisation | Machine Learning Techniques
Data Anomaly Detection
course content

Conteúdo do Curso

Data Anomaly Detection

Data Anomaly Detection

1. What is Anomaly Detection?
2. Statistical Methods in Anomaly Detection
3. Machine Learning Techniques

bookChallenge: Solving Task Using Regularisation

Tarefa
test

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Your task is to create a classification model using L2 regularization on the breast_cancer dataset. It contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The task associated with this dataset is to classify the breast mass as malignant (cancerous) or benign (non-cancerous) based on the extracted features.

Your task is to:

  1. Specify argument at the LogisticRegression() constructor:
    • specify penalty argument equal to l2;
    • specify C argument equal to 1.
  2. Fit regularized model on the training data.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 4
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bookChallenge: Solving Task Using Regularisation

Tarefa
test

Swipe to show code editor

Your task is to create a classification model using L2 regularization on the breast_cancer dataset. It contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The task associated with this dataset is to classify the breast mass as malignant (cancerous) or benign (non-cancerous) based on the extracted features.

Your task is to:

  1. Specify argument at the LogisticRegression() constructor:
    • specify penalty argument equal to l2;
    • specify C argument equal to 1.
  2. Fit regularized model on the training data.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 4
toggle bottom row

bookChallenge: Solving Task Using Regularisation

Tarefa
test

Swipe to show code editor

Your task is to create a classification model using L2 regularization on the breast_cancer dataset. It contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The task associated with this dataset is to classify the breast mass as malignant (cancerous) or benign (non-cancerous) based on the extracted features.

Your task is to:

  1. Specify argument at the LogisticRegression() constructor:
    • specify penalty argument equal to l2;
    • specify C argument equal to 1.
  2. Fit regularized model on the training data.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Tarefa
test

Swipe to show code editor

Your task is to create a classification model using L2 regularization on the breast_cancer dataset. It contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. The task associated with this dataset is to classify the breast mass as malignant (cancerous) or benign (non-cancerous) based on the extracted features.

Your task is to:

  1. Specify argument at the LogisticRegression() constructor:
    • specify penalty argument equal to l2;
    • specify C argument equal to 1.
  2. Fit regularized model on the training data.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 3. Capítulo 4
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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