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Consider the following performance metrics of a model. Precision = 0.95 Recall = 0.25
The F1 score will most likely to be
closer to 0.25
closer to 0.95
exactly in between 0.25 and 0.95
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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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