Should we treat outliers in target variable?
You should do Outlier Analysis of your target variable to prepare your training data for the model. Most model would perform better on noiseless data, as Outlier might skew the findings of your model in one direction.
What is the outlier variable?
An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. These points are often referred to as outliers.
When to do outlier analysis of target variable?
You should do Outlier Analysis of your target variable to prepare your training data for the model. Most model would perform better on noiseless data, as Outlier might skew the findings of your model in one direction. Generally, there is no need to perform normalization on target variable for model performance or accuracy.
Is it bad practice to remove outliers from data?
It’s bad practice to remove data points simply to produce a better fitting model or statistically significant results. If the extreme value is a legitimate observation that is a natural part of the population you’re studying, you should leave it in the dataset. I’ll explain how to analyze datasets that contain outliers you can’t exclude shortly!
Which is the simplest method to detect outliers?
One of the simplest methods for detecting outliers is the use of box plots. A box plot is a graphical display for describing the distribution of the data. Box plots use the median and the lower and upper quartiles.
Where are most of the outliers on the plot?
Looking at the plot above, we can most of data points are lying bottom left side but there are points which are far from the population like top right corner. The Z-score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured.