Machine Learning and Deep Learning

Distance Metric used in Machine Learning- ¢   Distance measures play an important role in machine learning. ¢   A distance measure is an objective score that summarizes the relative difference between two objects in a problem domain. ¢    The most famous algorithm of this type is the k-nearest neighbors algorithm, or KNN ¢   In the KNN algorithm, a classification or regression prediction is made for new examples by calculating the distance between the new example (row) and all examples (rows) in the training dataset. ¢    The k examples in the training dataset with the smallest distance are then selected and a prediction is made by averaging the outcome (mode of the class label or mean of the real value for regression).   Applications: ¢   K-Nearest Neighbors ¢   Learning Vector Quantization (LVQ) ¢   Self-Organizing Map (SOM) ¢   K-Means Clustering ¢   There are many kernel-based methods that may also be considered d...

    •       K-Nearest Neighbor is one of the simplest Machine Learning algorithms based on Supervised Learning techniques. •      K-NN algorithm assumes the similarity between the new case/data and available cases and puts the new case into the category that is most similar to the general categories. •       K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears it can be easily classified into a well-suited category by using the K-NN algorithm. •       K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for Classification problems •       K-NN is a  non-parametric algorithm , which means it does not make any assumption on underlying data. •       It is also called a  lazy learner algorithm  because it ...