Regression Analysis in Machine learning
Regression analysis is a statistical method to model the relationship between a
dependent (target) and independent (predictor) variables with one or more
independent variables. More specifically, Regression analysis helps us to
understand how the value of the dependent variable is changing corresponding to
an independent variable when other independent variables are held fixed. It
predicts continuous/real values such as temperature, age, salary, price, etc.
We can understand the concept of regression analysis using the below example:
Example: Suppose there is a marketing company A, who does various
advertisement every year and get sales on that. The below list shows the
advertisement made by the company in the last 5 years and the corresponding
sales:
Now, the company wants to do the advertisement of $200 in the year 2019 and
wants to know the prediction about the sales for this year. So to solve such type
of prediction problems in machine learning, we need regression analysis.
, Regression is a supervised learning technique which helps in finding the
correlation between variables and enables us to predict the continuous output
variable based on the one or more predictor variables. It is mainly used
for prediction, forecasting, time series modeling, and determining the causal-
effect relationship between variables.
In Regression, we plot a graph between the variables which best fits the given
datapoints, using this plot, the machine learning model can make predictions about
the data. In simple words, "Regression shows a line or curve that passes
through all the datapoints on target-predictor graph in such a way that the
vertical distance between the datapoints and the regression line is
minimum." The distance between datapoints and line tells whether a model has
captured a strong relationship or not.
Some examples of regression can be as:
o Prediction of rain using temperature and other factors
o Determining Market trends
o Prediction of road accidents due to rash driving.
Terminologies Related to the Regression Analysis:
o Dependent Variable: The main factor in Regression analysis which we
want to predict or understand is called the dependent variable. It is also
called target variable.
o Independent Variable: The factors which affect the dependent variables or
which are used to predict the values of the dependent variables are called
independent variable, also called as a predictor.
o Outliers: Outlier is an observation which contains either very low value or
very high value in comparison to other observed values. An outlier may
hamper the result, so it should be avoided.
o Multicollinearity: If the independent variables are highly correlated with
each other than other variables, then such condition is called
Multicollinearity. It should not be present in the dataset, because it creates
problem while ranking the most affecting variable.
o Underfitting and Overfitting: If our algorithm works well with the training
dataset but not well with test dataset, then such problem is
called Overfitting. And if our algorithm does not perform well even with
training dataset, then such problem is called underfitting.
Regression analysis is a statistical method to model the relationship between a
dependent (target) and independent (predictor) variables with one or more
independent variables. More specifically, Regression analysis helps us to
understand how the value of the dependent variable is changing corresponding to
an independent variable when other independent variables are held fixed. It
predicts continuous/real values such as temperature, age, salary, price, etc.
We can understand the concept of regression analysis using the below example:
Example: Suppose there is a marketing company A, who does various
advertisement every year and get sales on that. The below list shows the
advertisement made by the company in the last 5 years and the corresponding
sales:
Now, the company wants to do the advertisement of $200 in the year 2019 and
wants to know the prediction about the sales for this year. So to solve such type
of prediction problems in machine learning, we need regression analysis.
, Regression is a supervised learning technique which helps in finding the
correlation between variables and enables us to predict the continuous output
variable based on the one or more predictor variables. It is mainly used
for prediction, forecasting, time series modeling, and determining the causal-
effect relationship between variables.
In Regression, we plot a graph between the variables which best fits the given
datapoints, using this plot, the machine learning model can make predictions about
the data. In simple words, "Regression shows a line or curve that passes
through all the datapoints on target-predictor graph in such a way that the
vertical distance between the datapoints and the regression line is
minimum." The distance between datapoints and line tells whether a model has
captured a strong relationship or not.
Some examples of regression can be as:
o Prediction of rain using temperature and other factors
o Determining Market trends
o Prediction of road accidents due to rash driving.
Terminologies Related to the Regression Analysis:
o Dependent Variable: The main factor in Regression analysis which we
want to predict or understand is called the dependent variable. It is also
called target variable.
o Independent Variable: The factors which affect the dependent variables or
which are used to predict the values of the dependent variables are called
independent variable, also called as a predictor.
o Outliers: Outlier is an observation which contains either very low value or
very high value in comparison to other observed values. An outlier may
hamper the result, so it should be avoided.
o Multicollinearity: If the independent variables are highly correlated with
each other than other variables, then such condition is called
Multicollinearity. It should not be present in the dataset, because it creates
problem while ranking the most affecting variable.
o Underfitting and Overfitting: If our algorithm works well with the training
dataset but not well with test dataset, then such problem is
called Overfitting. And if our algorithm does not perform well even with
training dataset, then such problem is called underfitting.
