Table of Contents
Which method is robust to outliers?
Model-Based Methods Use a different model: Instead of linear models, we can use tree-based methods like Random Forests and Gradient Boosting techniques, which are less impacted by outliers. This answer clearly explains why tree based methods are robust to outliers.
How do you find outliers in machine learning?
Algorithm:
- Calculate the mean of each cluster.
- Initialize the Threshold value.
- Calculate the distance of the test data from each cluster mean.
- Find the nearest cluster to the test data.
- If (Distance > Threshold) then, Outlier.
How do you find outliers in data?
Multiplying the interquartile range (IQR) by 1.5 will give us a way to determine whether a certain value is an outlier. If we subtract 1.5 x IQR from the first quartile, any data values that are less than this number are considered outliers.
How are outliers detected?
Some of the most popular methods for outlier detection are: Z-Score or Extreme Value Analysis (parametric) Probabilistic and Statistical Modeling (parametric) Linear Regression Models (PCA, LMS)
How do I know if my data is robust?
Robust statistics, therefore, are any statistics that yield good performance when data is drawn from a wide range of probability distributions that are largely unaffected by outliers or small departures from model assumptions in a given dataset. In other words, a robust statistic is resistant to errors in the results.
What are the cost functions available for regression tasks?
Regression tasks deal with continuous data. Cost functions available for Regression are, Mean Absolute Error (MAE) is the mean absolute difference between the actual values and the predicted values. MAE is more robust to outliers. The insensitivity to outliers is because it does not penalize high errors caused by outliers.
What are the cost functions used in classification problems?
Cost functions used in classification problems are different than what we use in the regression problem. A commonly used loss function for classification is the cross-entropy loss. Let us understand cross-entropy with a small example.
What is the difference between Mae and Mae with outliers?
MAE is more robust to outliers. The insensitivity to outliers is because it does not penalize high errors caused by outliers. The drawback of MAE is that it isn’t differentiable at zero and many Loss function Optimization algorithms involve differentiation to find optimal values for Parameters.
What is the cost function in machine learning?
A Cost function is used to gauge the performance of the Machine Learning model. A Machine Learning model devoid of the Cost function is futile. Cost Function helps to analyze how well a Machine Learning model performs. A Cost function basically compares the predicted values with the actual values.