Linear classifiers A linear classifier has the form in 3D the discriminant is a plane, and in nD it is a hyperplane For a K NN classifier it was necessary to `carry the training data For a linear classifier, the training data is used to learn w and then discarded Only w is needed for classifying new data f=0 f=wgt;x + b

A classifier is the algorithm itself the rules used by machines to classify data. A classification model, on the other hand, is the end result of your classifiers machine learning. The model is trained using the classifier, so that the model, ultimately, classifies your data. There are both supervised and unsupervised classifiers ...

Inputs x are continuous feature vectors of length K, where j=1,,k and i=1,,n. So, the input matrix is X which contains N number of inputs each contains K number of features. Inputs can be illustrated as a matrix Xlike below. And output y is discrete and binary variable, such that y ϵ {0,1}. So, we can assume that y is Bernoulli distributed with probability parameter p.

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Lets we have a flipping/tossing a coin experiment. Supposing the coin is a fair one brings us equally likely outcomes of Head and Tail. That is the posterior probabilities are: where X is an input matrix and contains all trials/observations and their features. Since in this flipping coin experiment does not include any independent variable , our input matrix X includes only the trails we made, that is it will be a vector of n×1 where xis just symbolizing the first trial rather than an concrete input. But if we replace the experiment with a Credit Scoring one, our outcome universe will still be discrete and binary , however the input vector returns to a matrix again since some features has shown above! Another radical change expecting us after shifting the experiment is the uncertainty affecting the fairness that we assume for coins. Like unfair coins, credits are hosting different chances to be defaulted due to the different characte...

Below figure indicates the decision functions of most popular Linear Classifiers which are Perceptron, Linear Regression and Logistic Regression. In the graph we have lots of notations but do not worry, we will clarify all of them mathematically. Just for now, let me give their meanings: w weights/coefficients, s regression function, h hypothesis set that selected classifier brings, θ sigmoid/logistic function. Unfairness described in previous part brings the problem of uncertainty in the process and the necessity of anticipation. As a Supervised Classification method, Logistic Regression helps us to converge those uncertain posteriors with a differentiable decision function drawn in Figure 4 below. This function is called as logistic function or sigmoid function and helps us to shrink real valued continuous inputs into a range of which is gloriously useful while dealing with pro...

Like in other Machine Learning Classifiers, Logistic Regression has an objective function which tries to maximize likelihood function of the experiment. This approach is known as Maximum Likelihood Estimation MLE and can be written mathematically as follows. where 1. the output y {0,1}, 2. P is the posterior probability which is equal to 1/ and 3. parameters β is the vector of weights/coefficients in f : f as we defined earlier. Before describing and optimizing this objective with respect to parameter β, it may better to shift coin experiment in order to simplify remaining processes. So, the objective function of flipping a coin problem can be written in the below format. where p is the likelihood of success , y is independent Bernoulli random variable and the inner term is the joint likelihood distribution function of the experiment. We want to find the optimum value of p in order to maximize this function. But how did...

E.1.1. Coin Experiment, Average Learning

Not to be choky, lets leave the remaining parts of the article for a second postwhich I will share consecutively. All references and footnotes will be presented in the second and possibly the last post. Hope you to follow

Naive Bayes Classifier Support Vector Machines Decision Trees Boosted Trees Random Forest Nearest Neighbor Neural Networks Objective of Classification in Machine Learning . The types of classification algorithms in machine learning is used as per the model feasibility and ML engineer capability.

The objective of this paper is to present the process, results, and range of usability of a machine classification in a company that is producing discrete items.

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## Lecture 2: The SVM classifier

Linear classifiers A linear classifier has the form in 3D the discriminant is a plane, and in nD it is a hyperplane For a K NN classifier it was necessary to `carry the training data For a linear classifier, the training data is used to learn w and then discarded Only w is needed for classifying new data f=0 f=wgt;x + b