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In the chapter, we learnt that for training the model for a classification problem, we split the data in a ratio of 80:20. Why do we do this ?
so that we can train the model on 100% of data and test it on the 20% of data to measure its performance
so that we can train the model on 20% of the data and test it on the 80% of data to measure its performance
so that we can train the model on 80% of the data and test on the 20% of data to measure its performance
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1 Classification
2 Classification - MCQs - Example of classification
3 Classification - MCQs - Clustering set of students
4 Classification - MCQs - Supervised Learning and classification
5 Classification - MCQs - 80-20 split in classification problem
6 Classification - MCQs - Measure the performance of a classification model
7 Classification - MCQs - Definition - True positives and false positives-1
8 Classification - MCQs - Definition - True positives and false positives -2
9 Classification - MCQs - True positive also known as
10 Classification - MCQs - One Hot Encoding(OHE)
11 Classification - MCQs - MNIST - Multiclass , multi-label
12 Classification - MCQs - Precision and recall
13 Classification - MCQs - F1 score
14 Classification - MCQs - Case- SPAM. Calculations 1
15 Classification - MCQs - Case- SPAM. Calculations 2
16 Classification - MCQs - Case- SPAM. Calculations 3
17 Classification - MCQs - Case- SPAM. Calculations 4
18 Classification - MCQs - Case- SPAM. Calculations 5
19 Classification - MCQs - Case- SPAM. Calculations 6
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