The specificity metric in classification problems is defined by the following formula. Specificity = (True Negative)/(True Negative + False Positive) Specificity is the ideal metric when we need to minimize the false positives. This acts as the opposite of the recall metric.
In machine learning, standardization is a feature engineering technique by which the dataset features are re-scaled to achieve zero-mean value equal to zero (μ=0) and unit standard deviation value equal to one (σ=1). Each x value in the dataset gets a corresponding x' standardized value, which is calculated as follows. , where μ is the ... Read more
A stop word in machine learning text processing refers to any word which provides no content, such as simple and common words (and, to, so, by, to, etc)
Stratified cross validation is a data validation technique which is used when splitting the ML dataset into k subsets, of which k-1 subsets are used as training subsets (folds) and one (1) is used as the test subset (fold). This process is repeated k times. Stratified cross validation uses stratified sampling in the dataset, in ... Read more
Machine learning models and algorithms can be classified into three (3) major categories: Supervised learning, i.e. a type of machine learning in which known label values are provided as input so that a model can estimate these values in future datasets. Examples of supervised learning algorithms are regression and classification algorithms, such as linear regression ... Read more
SVM stands for Support Vector Machine. SVM is a well-known family of supervised learning non-parametric algorithms which are used in regression and classification machine learning problems, by separating data values using a hyperplane. SVM algorithms are ideal when there is presence of outliers in the model training data.
