L Suppose Required Classify Tumour S Benign Malignant Basis 9 Features Tumour Eg Uniformit Q43808136
l. Suppose you are required to classify a tumour a.s benign ormalignant on the basis of 9 features of the tumour, such as e.g.uniformity of cell size, clump thickness, mitosis, etc. You have adata set of 699 ca.se records of tumour. For each ca.se record of atumour, you have the 9 features describing the tumour, togetherwith correct classification of this tumour a.s benign ormalignant.
(a) Design a neural network model for the classificationtask.
(b) If the performance of your model on the training set isgood, but the test set performance is significantly worse, what isthe most likely problem? Discuss a simple way which may improveyour results.
(c) If you need even more accurate results, what kind ofapproaches would you try?
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Answer to l. Suppose you are required to classify a tumour a.s benign or malignant on the basis of 9 features of the tumour, such …
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