Regularization is essential in machine and deep learning. It is not a complicated technique and it simplifies the machine learning process. Setting up a machine-learning model is not just about feeding the data. When you are training your model through machine learning with the help of artificial neural networks, you will encounter numerous problems. These problems could affect output drastically. This article will help you understand the techniques you can use to reduce the problems during the machine learning process.
What is Regularization?
The use of regularization is the same as the name suggests. Regularization means making things acceptable or regular. Regularization is a technique that reduces error from a model by avoiding overfitting and training the model to function properly.
Overfitting is a common problem. When you overfeed the model with data that does not contain the capacity to handle, it starts acting irregularly. This irregularity will include noise instead of signal in the outcome. Your model will start considering the unnecessary data as the concept. The term used to refer to this is “overfitting”, and it leads to inaccurate outputs— decreasing the accuracy and efficiency of the data.
Suppose we need to predict if the newly graduated students will qualify for the interview. We will train our system with 20,000 resumes to see whether they qualified or not. The result we will get will be 99 percent accurate. Now, as you test your model with a completely different dataset, the outcome will be less than 50 percent. This happens because the model we are training does not generalize the outcome from unseen data. We can also see fitting in our everyday life.
Noise and Signal
The signal is a pattern that helps the model to learn the relevant data. However, noise is a random and irrelevant form of the data that you do not want to involve in the outcome. We do not want our models to include irrelevant data and affect our results. The reason behind this irregularity is the algorithm of the model. It learns and removes the errors during the training process.
Training for a longer period, even after solving all the errors, will decrease the performance, as the model will start learning irrelevant data. This will make our model complicated, and it fails to generalize new data. A good algorithm will separate noise and signal.
How Regularization Works
The main reason why the model is “overfitting” is that it fails to generalize the data because of too much irrelevance. However, regularization is an effective method that improves the accuracy of the model and reduces unnecessary variances.
Furthermore, this technique also avoids losing important data, which happens with under fitting. Regularization helps the model to learn by applying previously learned examples to the new unseen data. You can also reduce the model capacity by driving various parameters to zero. Regularization will remove additional weights from specific features and distribute those weights evenly.
Let us understand how it works. When we want the model to work properly, we define the loss function. This loss function will define the performance of the model according to the data by loss calculation. We need to minimize the loss to find the model we want. For this, regularization adds lambda to penalize the loss function. We get the optimum solution from this technique as it rejects high training errors with smaller lambda values and rejects higher complexity models with higher lambda values.
Types of Regularization Techniques
The regression model of this regularization technique is called Lasso Regression. The regression model is a penalty term. Lasso is short for the Least Absolute Shrinkage and Selection Operator. Lasso adds the magnitude’s absolute value to the coefficient. These values are penalty terms of the loss function.
On the other hand, the regression model of L2 regularization is ridge regression. In this regularization, the penalty term of the loss function is the squared magnitude of the coefficient. In this method, the value of lambda is zero because adding a large value of lambda will add more weights, causing underfitting.
Choosing Between L1 and L2 Regularization
To choose the regularization technique between L1 and L2, you need to consider the amount of the data. If the data is larger, you should use L2 regularization. However, if the data is small, you need to choose the L1 regularization.
According to Wikipedia, dropout means dropping visible or hidden units. In easy words, drop out means ignoring the units or neurons while training the model. The model will not consider these units when passing the data through an artificial neural network. This will avoid overfitting the training data.
In the data augmentation technique, you increase the size of relevant data or signal that you want to include in the output. The main reason why the model is not generalizing is because of overfitting. However, when the size of the relevant data increases, the model will not consider adding noise.
When we are training our model through supervised machine learning, we feed training data. Now the model will learn through patterns of the training data. We expect that the model only defines patterns through signal, which is relevant data. However, the model also includes noise. This affects the performance of the model while going through new data.
That is where the regularization technique helps. It reduces the complexity by adding a penalty. There are two common types of regularization techniques. L1 will minimize the value of the weights, and L2 will minimize the squared magnitude. However, there are two more techniques for avoiding overfitting, one is “drop out” and the other is “data augmentation”. Drop out will ignore the irrelevant units or noise, and data augmentation will increase the size of the signal.