What is K-Means Clustering
Published in
Artificial Intelligence in Plain English
Clustering is similar to classification but the difference
is we don’t know what are we looking for. Clustering means
grouping identical data into clusters or segments. We use a
clustering algorithm to find out the similarity and to
classify the data into different clusters.
What Is Naive Bayes?
Published in The Startup
Naive Bayes is a classification technique based on an
assumption of independence between predictors which is known
as Bayes’ theorem. In simple terms, a Naive Bayes classifier
assumes that the presence of a particular feature in a class
is unrelated to the presence of any other feature.
Random Forest
Published in Analytics Vidhya
Random Forest is a supervised learning algorithm. Like you
can already see from it’s name, it creates a forest and
makes it somehow random. The forest it builds, is an
ensemble of Decision Trees, most of the time trained with
the bagging method.
Support Vector Machine
Published in The Startup
Support Vector Machine is a supervised machine learning
algorithm.The goal of the SVM is to train a model that
assigns new unseen objects into a particular category.
Decision Tree Regression
Published in The Startup
Decision Tree is a supervised machine learning algorithm and
it is one of the popular machine learning algorithm. It is a
tree like structure constructed on the basis of
attributes/features . Decision Trees is the non-parametric
supervised learning approach.
K Nearest Neighbors
Published in Analytics Vidhya
KNN falls in the supervised learning family of algorithms. K
nearest neighbors is a simple algorithm that stores all
available cases and classifies new cases based on a
similarity measure.
Logistic Regression
In linear Regression, the goal is to find the best fit line
that can accurately predict the output for the continuous
dependent variable.Linear Regression predicts the
probability of outcome can exceed 0 and 1 range but the
probability range is 0 to 1 it means that the parts of the
line that are above y= 1 and below y=0 does not make any
sense in reference to logistic regression.
Support Vector Regression
Published in Analytics Vidhya
The goal of the SVM is to train a model that assigns new
unseen objects into a particular category. It achieves this
by creating a linear partition of the feature space into two
categories.
Decision Trees Classification
Published in Analytics Vidhya
Decision tree is one of the most popular machine learning
algorithms. It is basically tree like structure constructed
on the basis of attributes/features . Decision Trees is the
non-parametric supervised learning approach.
Linear Regression
Published in Analytics Vidhya
Linear Regression is one of the most simple Machine learning
algorithm used for solving regression problems. It is used
for predicting the continuous dependent variable with the
help of independent variables.The goal of the Linear
regression is to find the best fit line that can accurately
predict the output for the continuous dependent variable.
