What is Regularisation in Machine Learning
In machine learning, the focus is to equip appliances to execute specific tasks without detailed instructions. Here the systems are programmed to learn and enhance automatically from their experience.
When it comes to the accuracy of the machine learning model, overfitting or underfitting is mainly the reason for the poor performance. Data science professionals commonly use the concept of regularisation to adjust machine learning models. In this article, you will learn all about regularisation and its types to prevent the phenomenon of overfitting.
What is Overfitting in Machine Learning?
To prepare a particular machine learning model, some data is to be fed to the model to learn from. The model is considered to be the best fit when it rectifies all the necessary information and bypasses the noise (unnecessary patterns and random data points).
Now there is a condition when the machine learning model fits very well with the data set and starts learning noise along with important patterns. Here the model tries to match each data point on the curve leading to overfitting. This hinders the performance when new data is applied to the model.
On the contrary, when the machine learning model cannot locate suitable patterns with the test dataset. Here also, the model is unable to predict a new data point. This condition is called underfitting.
What is Regularisation in Machine Learning?
Regularisation in machine learning is an approach wherein information is added to the model to prevent overfitting. In this process, the coefficient estimate is minimised to zero, to lower the capability of the overfitted model.
This implies that regularisation limits the learning of a model as the more flexible a model is, the more freedom it possesses to fit as many data points as possible. Thus, the risk of overfitting is reduced by preventing the model from learning.
Types of Regularisations in Machine Learning
From the previous header, it is clear, how regularisation is used to avoid overfitting in machine learning. L2(Ridge regression) and L1(Lasso regression) are the typical types of regularisations used in ML (Machine Learning). However, there are other types as well.
Lasso Regression
L1 Regularisation or Lasso (Least Absolute Shrinkage and Selector Operator) Regression is a linear regression method used to shrink the cost function. In this process penalty term (summation of absolute values of the coefficients) is added to the cost function.
As the absolute value of the coefficients is employed, it makes them equal to zero, reducing overfitting.
Ridge Regularisation
In L2 Regularisation or Ridge Regularisation, the penalty term that is added to the cost function is the summation of the squared value of coefficients.
The application of squared values of the coefficient forces all coefficients value near to zero but not precisely equal to zero assisting in reducing overfitting. It also improves the interpretability of the machine learning model.
Early Stopping Regularisation
In this approach, one part of the training set is taken as the validation set, and the performance of the machine learning model is measured against this validation set. Here the activity on the ML model is instantly halted when the performance of the considered validation set gets worse.
Dropout Regularisation
Dropout regularisation is applied when a machine learning model is in a neural network. In this process, the input passes through the neural network layer reaching the output layer, used for prediction.
In a neural network, there are several nodes at each layer, and the nodes between two subsequent layers are connected. In the dropout process, links between the nodes of successive layers are randomly dropped as per the dropout ratio and the rest of the network is introduced in the current iteration. Similarly, in the next stage, another node is dropped randomly as this process continues.
Balancing Bias and Variance to Avoid Overfitting
When an algorithm has restricted flexibility to learn from data, bias occurs. In such a model there is a high error in training and test data, causing underfitting. A model with high variance performs excellently on training data but shows high error rates on test data. In a particular model, a high variance induces overfitting.
An optimal model is when bias and variance are at optimal levels. A right ‘bias-variance’ balance can be achieved by selecting an appropriate statistical learning method.
While this article has tried to explain what is overfitting in machine learning, an in-person learning experience makes a more significant difference.
To better understand machine learning, data science, data analytics and related technologies, you can enrol on Emeritus India’s machine learning courses. These courses will help you understand various ML concepts comprehensively and help you determine the best models to suit different business conditions.
