Predicts the probability of the outcome class. The algorithm finds the linear relationship between the independent
variables and a link function of these probabilities.
Ordinary Least Squares Technique to find the best fit line. The best fit line is the line which has minimum square deviations from all the data points to the line.
Decision Trees are Nonparametric hierarchical model, that works on a divide & conquer strategy, a rule-based algorithm that works on the principle of recursive partitioning.
KNN is based on the calculating distance among the various points. The distance can be any of the distance measures such as Euclidean distance discussed in previous sections.
The same concept underlies Relationship Mining, Market Basket Analysis, and Affinity Analysis: how are two entities connected to one another and is there any reliance between them.
Agglomerative technique (top-down hierarchy of clusters) or Divisive technique (bottom-up hierarchy of clusters) are other names for hierarchical clustering.
Similar records to be grouped together. High intra-class similarity, Dissimilar records to be assigned to different groups. Less inter-class similarity
If the outcome variable 'Y' in the historical data is known, then supervised learning tasks are applied to the historical data. Predictive modelling and machine learning are other names for supervised learning.
Feature Extraction and Feature Engineering are other names for attribute generation. Try to use domain expertise to create more insightful derived variables from the provided variables.
In this blog, Unleash the Potential of Data The Tidyverse is a game-changing collection of R utilities that revolutionises data manipulation, visualisation, and analysis..
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