Course Content
Data Anomaly Detection
Data Anomaly Detection
Challenge: Solving Task Using Regularisation
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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:
- Specify argument at the
LogisticRegression()
constructor:- specify
penalty
argument equal tol2
; - specify
C
argument equal to1
.
- specify
- Fit regularized model on the training data.
Thanks for your feedback!
Challenge: Solving Task Using Regularisation
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:
- Specify argument at the
LogisticRegression()
constructor:- specify
penalty
argument equal tol2
; - specify
C
argument equal to1
.
- specify
- Fit regularized model on the training data.
Thanks for your feedback!
Challenge: Solving Task Using Regularisation
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:
- Specify argument at the
LogisticRegression()
constructor:- specify
penalty
argument equal tol2
; - specify
C
argument equal to1
.
- specify
- Fit regularized model on the training data.
Thanks for your feedback!
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:
- Specify argument at the
LogisticRegression()
constructor:- specify
penalty
argument equal tol2
; - specify
C
argument equal to1
.
- specify
- Fit regularized model on the training data.