## What is Type I Error?

Type I Error in statistical hypothesis testing is the incorrect rejection of a true null hypothesis (a false positive). More simply stated, a type I

Read more## What is True Positive Rate (Sensitivity)?

True Positive Rate (Sensitivity) is a statistical measure which measures the proportion of positives that are correctly identified as such (for example, the percentage of

Read more## What is True Negative Rate (Specificity)?

True Negative Rate (Specificity) is a statistical measure which measures the proportion of negatives that are correctly identified as such (for example, the percentage of

Read more## What is Three Sigma Rule?

Three Sigma Rule in the empirical sciences express a conventional heuristic that “nearly all” values are taken to lie within three standard deviations of the

Read more## What is Support Vector Machines (SVM)?

Support Vector Machines (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm

Read more## What is Supervised Learning?

Supervised Learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples.

Read more## What is Statistical Significance?

Statistical Significance in statistical hypothesis testing is attained whenever the observed p-value of a test statistic is less than the significance level defined for the

Read more## What is Statistical Power?

Statistical Power of any test of statistical significance is defined as the probability that it will reject a false null hypothesis. Statistical power is inversely

Read more## What is Sentiment Analysis?

Sentiment Analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states

Read more## What is Semi-Supervised Learning?

Semi-Supervised Learning is a class of supervised learning tasks that also make use of unlabeled data for training – typically a small amount of labeled

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