Predictive analytics is the practice of using data mining and machine learning techniques to make predictions about future events. Predictive analytics can be used to predict things such as how likely a customer is to churn, or what the probability is that a particular disease will occur in a population.
Definition of Interpretability Interpretability: Interpretability is a measure of how easily a model’s predictions can be explained to humans. Models that are easy to interpret are more likely to be trusted and used in decision-making processes. Why does Interpretability matter? Interpretability is an important factor in how effective data science and machine learning tools are….
Definition of Multivariate Multivariate: Multivariate refers to a dataset or analysis that considers more than one variable at a time. For example, in a multivariate analysis, you might examine the relationship between height and weight in order to understand how they are related. How is Multivariate used? Multivariate analysis is a collection of techniques used…
Definition of Oblique Rotation Oblique Rotation: Oblique rotation is a type of rotation that is not perpendicular to the plane of rotation.
Definition of False Negative False Negative: False negative is a result of a test where a condition that is true is incorrectly reported as being false. What are the impacts of False Negatives? False Negatives can have a significant impact and be costly to data science and machine learning projects. False Negatives refer to instances…
Definition of MATLAB MATLAB is a software suite for high-performance numerical computation, visualization, and programming. It integrates mathematical computing, simulation, and graphical output into a single software environment. MATLAB is used extensively in engineering and scientific fields. How is MATLAB used? MATLAB is a high-level programming language developed by MathWorks for numerical computing and data…
Definition of Kernel Regression Kernel Regression: Kernel regression is a type of nonlinear regression that uses a kernel function to calculate the weight of each input data point. This type of regression is often used for time series data, where the input points are close together in time. How is Kernel Regression used? Kernel Regression…