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

Course Content

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

Task
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.

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Section 3. Chapter 4
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bookChallenge: Solving Task Using Regularisation

Task
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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 4
toggle bottom row

bookChallenge: Solving Task Using Regularisation

Task
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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Task
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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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