ROC Curve and AUC

ROC curves and AUC are used to measure performance in machine earning. They are the most widely used evaluation metrics for checking any classification model’s performance. It tells how much the model is capable of distinguishing between classes. ROC (Receiver Operator Characteristic Curve) is a probability curve and AUC represents the…

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Confusion Matrix

Confusion matrix, which is also known as error matrix is a designated table that is used to measure the precise performance of machine learning algorithm. Calculation of a confusion matrix can give you a better insight as to whether your classification model is getting right and the sort of error…

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Logistic Regression

Logistic Regression is a supervised classification algorithm that is used to predict the probability of a categorical dependent variable using a given set of independent variables. It is a predictive analysis algorithm and based on the concept of probability. The most common use of logistic regression models is in binary classification problems. Some…

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Gauss Markov Theorem: OLS is BLUE!

The Gauss-Markov theorem states that if your linear regression model satisfies the classical assumptions, then ordinary least squares (OLS) regression produces best linear unbiased estimates (BLUE) that have the smallest variance of all possible linear estimators. There are five Gauss Markov assumptions: Linearity: the parameters we are estimating using the OLS method must be themselves linear. Random:…

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Reinforcement Learning

The reason why I included reinforcement learning in this article, is that one might think that “supervised” and “unsupervised” encompass every ML algorithm, and it actually does not. There are algorithms that aren’t supervised nor unsupervised, like Reinforcement Learning. Reinforcement learning is the field that studies the problems and techniques…

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ML Algorithms

I will discuss the most popular machine learning algorithms. There are so many algorithms available and I categorized algorithms based on the learning style. Let’s take a look at four different learning styles in machine learning algorithms: Supervised Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning Here is the list of…

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Supervised Learning

Supervised learning is a class of ML where machines can learn through examples. The historical data set has both input and output (also called target or response) values. Based on the training data set, the machine already has some idea of what the target value would look like, which kind…

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