KNN algorithm is non-parametric, meaning it doesn't make an underlying assumption of data distribution. In the KNN classifier: We find K-neighbors nearest to the white point. In the example below, we chose k=5.
Low bias: SVM, decision trees, KNN algorithm, etc. High bias: Linear and logistic regression 63. What is the use of Hash tables in statistics? Hash tables are the data structures that are used to denote the representation of key-value pairs in a structured way. The hashing function is us...
KNN is a classification algorithm It classifies an unlabeled observation based on its K (can be any number) surrounding neighbors 17. What Is ‘naive’ in the Naive Bayes Classifier? The classifier is called ‘naive’ because it makes assumptions that may or may not turn out to be correct....
32. Why is KNN preferred when determining missing numbers in data? K-Nearest Neighbour (KNN) is preferred here because of the fact that KNN can easily approximate the value to be determined based on the values closest to it. 33. How can one handle suspicious or missing data in a dataset...
KNN is a non-parametric algorithm used for classification and regression tasks. It works by finding the K nearest data points in the training set to a given test point and then making predictions based on the majority class (classification) or the mean value (regression) of those K neighbors...
engineer’s ability to develop inexpensive methods for transferring large amounts of data is of particular importance. If you’re responsible for a database with potentially millions (let alone billions) of records, finding the most efficient solution is essential. Commonalgorithm interview questions...
In the case of loan approval, for instance, the KNN algorithm is a good Supervised Learning algorithm because it effectively sorts applications into two classes: approved and disapproved. The candidate's answer should touch on use cases that show they are knowledgeable in your field. It should ...
Hypothesis Testing: Permutation tests rely on resampling for statistical inferences. Data Science Probability Interview Questions 75. What do you understand by Hypothesis in the context of Machine Learning? In machine learning, a hypothesis represents a mathematical function that an algorithm uses to repr...
Support Vector Machines (SVM): A powerful, versatile classification technique that works well on both linear and non-linear data. K-Nearest Neighbors (KNN): A simple, effective classification algorithm that stores all available cases and classifies new cases based on a similarity measure. Clustering...
10) How is KNN different from k-means clustering? K-Nearest Neighbours is a supervised algorithm, and k-means clustering is an unsupervised algorithm. For the K-nearest neighbors to work, we require labeled data to classify the unlabeled point. K-means clustering needs only a threshold and a...