What is a support vector machine (SVM) and what are its key components? - Support Vector machines are a type of Supervised algorithm which can be used for both Regression and Classification problems. In SVMs, the main goal is to find a hyperplane which will be used to segregate different data points into classes. Any new data point will be classified based on this defined hyperplane. Support Vector machines are highly effective when dealing with high dimensionality space and can handle non linear data very well. But if the number of features are greater than number of data samples, it is susceptible to overfitting. The key components of SVM are:   (1). Kernels Function: It is a mapping function used for data points to convert it into high dimensionality feature space. (2). Hyperplane: It is the decision boundary which is used to differentiate between the classes of data points. (3). Margin: It is the distance between Support Vector and Hyperplane. (4). C: It is a regularization parameter which is used for margin maximization and misclassification minimization., What is the Naïve Bayes algorithm and what are the different assumptions of Naive Bayes? - The k-Nearest Neighbors (KNN) algorithm is a simple and versatile supervised machine learning algorithm used for both classification and regression tasks. KNN makes predictions by memorizing the data points rather than building a model about it. This is why it is also called “lazy learner” or “memory based” model too….. KNN relies on the principle that similar data points tend to belong to the same class or have similar target values. This means that, In the training phase, KNN stores the entire dataset consisting of feature vectors and their corresponding class labels (for classification) or target values (for regression). It then calculates the distances between that point and all the points in the training dataset. (commonly used distance metrics are Euclidean distance and Manhattan distance)…. (Note : Choosing an appropriate value for k is crucial. A small k may result in noisy predictions while a large k can smooth out the decision boundaries. The choice of distance metric and feature scaling also impact KNN’s performance.),

Machine Learning _ Intermediate Level Questions

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