k-NN Algorithm.pptx
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The document provides an introduction to the k-nearest neighbors (k-NN) machine learning algorithm. It discusses how k-NN is used for classification problems by identifying the closest training examples in a data set. It also describes how k-NN can be used to classify new data points based on their distance to labeled examples, and mentions some common applications of k-NN like recommender systems, computer vision, and genetic data analysis. Finally, it gives a brief example of how k-NN could be used to classify different food items based on their measured properties.
k-NN Algorithm.pptx
- 1. Intro to k-NN USED IN CLASSIFICATION PROBLEMS
- 3. Lets Talk about the Movies
- 4. Identify the Genre and Group them
- 5. Cluster – Action Vs Comedy Action Comedy Comedy & Action
- 6. Hotstar Page – a Good Example of K-NN Thriller | Action | Drama | Romance | Comedy
- 7. Nearest Neighbor Classification The Nearest Neighbors are defined by their characteristics of class and using them to classify the unlabeled set of data Suitable for Classification Tasks where the relationship between features and target class are numerous, complex and extremely difficult to understand. Computer Vision Applications Optical Character Recognition Predicting if a person will enjoy music or a movie based on recommendations Patterns in Genetic Data Detecting Diseases
- 8. Identify the Class of Red Star
- 9. Identify the Class of Red Star
- 10. What k- NN does Whether a new point should be Classified as Red Point or Green Point, This is where k-NN comes.
- 11. Euclidean Distance Default Value of ‘K’ is 05
- 12. Brute Force How do we choose neighbours? Ans. Brute Force Lets consider for simple case with two dimension plot. If we look mathematically, the simple intuition is to calculate the Euclidean distance from point of interest ( of whose class we need to determine) to all the points in training set. Then we take class with majority points. This is called Brute Force method. Remember that Brute Force performs worst when there are large dimensions and large training sets. With larger dimensions, it will take longer time. This is called the “curse of dimensionality”.
- 15. Blind Taste Experience case study Blind Taste Experience involves some people going in a restaurant and tasting food in darkness. In Mystery Meal, people are asked to mark the food on two parameters – Crunchy and Sweet Scale used – 1 to 10 (10 being highest and 01 being lowest) The food products are labeled as follows:
- 16. Tomato Family Notice the pattern of Veggies, Fruits and Proteins Locating the tomato’s nearest neighbor requires a distance formula. k-NN uses EUCLIDEAN DISTANCE to find the answer
- 18. How does It do that??
- 19. Interview Questions In the given image, which would be the best value for k assuming that the algorithm you are using is k- Nearest Neighbour. A) 3 B) 10 C) 20 D) 50
- 20. Interview Questions In the given image, which would be the best value for k assuming that the algorithm you are using is k- Nearest Neighbour. A) 3 B) 10 C) 20 D) 50 Solution: B Validation error is the least when the value of k is 10. So it is best to use this value of k
- 21. Interview Question Which of the following option is true about k-NN algorithm? A) It can be used for classification B) It can be used for regression C) It can be used in both classification and regression
- 22. Interview Question Which of the following option is true about k-NN algorithm? It can be used for classification B) It can be used for regression C) It can be used in both classification and regression Solution: C We can also use k-NN for regression problems. In this case the prediction can be based on the mean or the median of the k-most similar instances.
- 23. Interview Question Which of the following statement is true about k-NN algorithm? A) K-NN performs much better if all of the data have the same scale B) K-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large C) K-NN makes no assumptions about the functional form of the problem being solved A) 1 and 2 B) 1 and 3 C) Only 1 D) All of the above
- 24. Interview Question Which of the following statement is true about k-NN algorithm? A) K-NN performs much better if all of the data have the same scale B) K-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large C) K-NN makes no assumptions about the functional form of the problem being solved A) 1 and 2 B) 1 and 3 C) Only 1 D) All of the above Solution: D The above mentioned statements are assumptions of K-NN algorithm
- 25. Interview Question Which of the following machine learning algorithm can be used for imputing missing values of both categorical and continuous variables? A) k-NN B) Linear Regression C) Logistics Regression
- 26. Interview Question Which of the following machine learning algorithm can be used for imputing missing values of both categorical and continuous variables? A) k-NN B) Linear Regression C) Logistics Regression Solution: A k-NN algorithm can be used for imputing missing value of both categorical and continuous variables.
- 27. Interview Question Which of the following distance measure do we use in case of categorical variables in k-NN? A) Hamming Distance B) Euclidean Distance C) Manhattan Distance
- 28. Interview Question Which of the following distance measure do we use in case of categorical variables in k-NN? A) Hamming Distance B) Euclidean Distance C) Manhattan Distance Solution: A Both Euclidean and Manhattan distances are used in case of continuous variables, whereas hamming distance is used in case of categorical variable.
- 29. Interview Question A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong? Note: Model has successfully deployed and no technical issues are found at client side except the model performance A) It is probably a overfitted model B) It is probably a underfitted model C) Can’t say D) None of these
- 30. Interview Question A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong? Note: Model has successfully deployed and no technical issues are found at client side except the model performance A) It is probably a overfitted model B) It is probably a underfitted model C) Can’t say D) None of these Solution: A In an overfitted module, it seems to be performing well on training data, but it is not generalized enough to give the same results on a new data.