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Please enable JavaScript in your browser settings or update your browser.Challenge: Solving Task Using Regularisation | Machine Learning Techniques
Data Anomaly Detection
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Course Content

Data Anomaly Detection

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

Challenge: Solving Task Using Regularisation

Task

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.

Task

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 desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 4
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Challenge: Solving Task Using Regularisation

Task

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.

Task

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 desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 4
toggle bottom row

Challenge: Solving Task Using Regularisation

Task

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.

Task

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 desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Task

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