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Please enable JavaScript in your browser settings or update your browser.Challenge: Solving Task Using Regularisation | Machine Learning Techniques
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Зміст курсу

Data Anomaly Detection

1. What is Anomaly Detection?

General InformationTypes of AnomaliesHow Outliers Influence On Prediction ResultsWhat Should We Do With Detected Outliers

2. Statistical Methods in Anomaly Detection

Rule-based ApproachChallenge: Rule-based Approach1.5 IQR Rule3-Sigma RuleMedian Absolute DeviationChallenge: Outlier Detection Using MAD Rule

3. Machine Learning Techniques

ClusteringChallenge: Using DBSCAN Clustering to Detect OutliersRegularisationChallenge: Solving Task Using RegularisationAutoencoders

Data Anomaly Detection

Challenge: Solving Task Using RegularisationChallenge: Solving Task Using Regularisation

Завдання

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.

Все було зрозуміло?

Секція 3. Розділ 4
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course content

Зміст курсу

Data Anomaly Detection

1. What is Anomaly Detection?

General InformationTypes of AnomaliesHow Outliers Influence On Prediction ResultsWhat Should We Do With Detected Outliers

2. Statistical Methods in Anomaly Detection

Rule-based ApproachChallenge: Rule-based Approach1.5 IQR Rule3-Sigma RuleMedian Absolute DeviationChallenge: Outlier Detection Using MAD Rule

3. Machine Learning Techniques

ClusteringChallenge: Using DBSCAN Clustering to Detect OutliersRegularisationChallenge: Solving Task Using RegularisationAutoencoders

Data Anomaly Detection

Challenge: Solving Task Using RegularisationChallenge: Solving Task Using Regularisation

Завдання

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.

Все було зрозуміло?

Секція 3. Розділ 4
toggle bottom row
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