These results are discussed in detail in the evaluation section below. The k-NN is a type of lazy learning where the function is only approximated locally and all computation I tried different models like KNN, SVM, Decision Tree, Naive Bayes by changing the hyper-parameters values.

Naive Bayes classifiers are computationally fast when making decisions.
Which Classification Algorithm among SVM, Naive Bayes, Decision tree, and K-NN is better for classifying large size textual documents? All these differences being statistically significant. They are probabilistic, which means that they calculate the probability of each tag … The nonlinearity of kNN is intuitively clear when looking at examples like Figure 14.6.The decision boundaries of kNN (the double lines in Figure 14.6) are locally linear segments, but in general have a complex shape that is not equivalent to a line in 2D or a hyperplane in higher dimensions..

I plotted the graph of model accuracy vs different parameter to visualize it clearly. An example of a nonlinear classifier is kNN. That's during the structure learning some crucial attributes are discarded.

It is a supervised learning problem where you know the class for a set of a training data points and need to propose the class for any other given data point.

Introduction. Algoritma Naive Bayes Merupakan pengklasifikasian statistik yang dapat digunakan untuk memprediksi probabilitas keanggotaan suatu class. Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. When you say you built it you designed a Naive Bayes model from looking at tabular data vs. allowing the computer to build that Naive Bayes model for you from tabular data.

LR performs better than naive bayes upon colinearity, as naive bayes expects all features to be independent. Logistic Regression vs KNN : KNN is a non-parametric model, where LR is a parametric model. Naïve Bayes has a naive assumption of conditional independence for every feature, which means that the algorithm expects the features to be independent which not always is the case. Naive bayes works well with small datasets, whereas LR+regularization can achieve similar performance.

Experiments show that kSS obtains an average f 1 score of 0.87, outperforming other popular Machine Learning methods such as Artificial Neural Networks (0.80), Decision Trees (0.75), other kNN variants (0.79), naïve Bayes (0.72) and Support Vector Machines (0.82). Naive Bayes is for classification.
Bayesian Classification didasarkan pada teorema Bayes yang memiliki kemampuan klasifikasi serupa decision tree dan neural network.

KNN doesn't always win, but in some cases it has been shown to outperform both models. Naive Bayes is a family of probabilistic algorithms that take advantage of probability theory and Bayes’ Theorem to predict the tag of a text (like a piece of news or a customer review). Bayesian Network is more complicated than the Naive Bayes but they almost perform equally well, and the reason is that all the datasets on which the Bayesian network performs worse than the Naive Bayes have more than 15 attributes.



3) Optimal Data set selection:


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