- Supervised Learning
- Classification
- Regression
- Time Series Forecasting
- Unsupervised Learning
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- Semi-Supervised Learning
- Reinforcement Learning(ML)
- Deep Learning(ML)
- Transfer Learning(ML)
- Ensemble Learning
- Explainable AI (XAI)
- Bayesian Learning
- Decision Trees
- Support Vector Machines (SVMs)
- Instance-Based Learning
- Rule-Based Learning
- Neural Networks
- Evolutionary Algorithms
- Meta-Learning
- Multi-Task Learning
- Metric Learning
- Few-Shot Learning
- Adversarial Learning
- Data Pre Processing
- Natural Language Processing(ML)
Classification QUIZ QUESTIONS
Question: 1
Which of the following metrics are used to evaluate classification models?
Question: 2
Which one is a classification algorithm?
Question: 3
Classification is-
Question: 4
You have a dataset of different flowers containing their petal lengths and color. Your model has to predict the type of flower for given petal lengths and color. This is a-
Question: 5
A classifier-
Question: 6
Classification is appropriate when you-
Question: 7
With the help of a confusion matrix, we can compute-
Question: 8
What does recall refer to in classification?
Question: 9
False negatives are-
Question: 10
Suppose your classification model predicted true for a class which actual value was false. Then this is a-
Question: 11
The false negative value is 5 and the true positive value is 20. What will be the value of recall-
Question: 12
The true positive value is 10 and the false positive value is 15. Calculate the value of precision-
Question: 13
If the precision is 0.6 and the recall value is 0.4. What will be the f measure?
Question: 14
Which one is a different algorithm?
Question: 15
What is a support vector?
Question: 16
Which of the following is a lazy learning algorithm?
Question: 17
Which of the following is not a lazy learning algorithm?
Question: 18
What is the most widely used distance metric in KNN?
Question: 19
Which of the following is the best algorithm for text classification?
Question: 20
What does k stand for in the KNN algorithm?
Question: 21
Support Vector Machine is-
Question: 22
What are hyperplanes?
Question: 23
What is a kernel?
Question: 24
Which of the following is not a kernel?
Question: 25
Why Naive Bayes is called naive?
Question: 26
For two events A and B, the Bayes theorem will be-
Question: 27
How does a decision tree work?
Question: 28
Suppose you have a dataset that is randomly distributed. What will be the best algorithm for that dataset?
Question: 29
Which pair of the algorithms are similar in operation?
Question: 30
Which metric is not used for evaluating classification models?