AI News, What you need to know about data augmentation for machinelearning

What you need to know about data augmentation for machinelearning

Plentiful high-quality data is the key to great machine learning models.

But good data doesn’t grow on trees, and that scarcity can impede the development of a model.

Smart approaches to programmatic data augmentation can increase the size of your training set 10-fold or more.

Even better, your model will often be more robust (and prevent overfitting) and can even be simpler due to a better training set.

The simplest approaches include adding noise and applying transformations on existing data.

Imputation and dimensional reduction can be used to add samples in sparse areas of the dataset.

More advanced approaches include simulation of data based on dynamic systems or evolutionary systems.

In this post we’ll focus on the two simplest approaches: adding noise and applying transformations.

In many datasets we expect that there is unavoidable statistical noise due to sampling and other factors.

Doing so can make the model more robust, although we need to take care when constructing the noise term.

In my function approximation example, I demonstrated creating a simple neural network in Torch to approximate a function.

However, in my simple workflow for deep learning, I said I prefer using R for everything but training the model.

Using this approach, it’s possible to create a 20 hidden node network that performs as well as the 40 node network in the earlier post.

Think about this: by adding noise (and increasing the size of the training set), we’ve managed to reduce the complexity of the network 2-fold.

How to handle Imbalanced Classification Problems in machine learning?

If you have spent some time in machine learning and data science, you would have definitely come across imbalanced class distribution.

This problem is predominant in scenarios where anomaly detection is crucial like electricity pilferage, fraudulent transactions in banks, identification of rare diseases, etc.

Finally, I reveal an approach using which you can create a balanced class distribution and apply ensemble learning technique designed especially for this purpose.

Utility companies are increasingly turning towards advanced analytics and machine learning algorithms to identify consumption patterns that indicate theft.

For any imbalanced data set, if the event to be predicted belongs to the minority class and the event rate is less than 5%, it is usually referred to as a rare event.

Ex: In an utilities fraud detection data set you have the following data: Total Observations = 1000 Fraudulent  Observations = 20 Non Fraudulent Observations = 980 Event Rate= 2 % The main question faced during data analysis is –

For eg: A classifier which achieves an accuracy of 98 % with an event rate of 2 % is not accurate, if it classifies all instances as the majority class.

Thus, to sum it up, while trying to resolve specific business challenges with imbalanced data sets, the classifiers produced by standard machine learning algorithms might not give accurate results.

Apart from fraudulent transactions, other examples of a common business problem with imbalanced dataset are: In this article, we will illustrate the various techniques to train a model to perform well against highly imbalanced datasets.

And accurately predict rare events using the following fraud detection dataset: Total Observations = 1000 Fraudulent   Observations =20 Non-Fraudulent Observations = 980 Event Rate= 2 % Fraud Indicator = 0 for Non-Fraud Instances Fraud Indicator = 1 for Fraud

Dealing with imbalanced datasets entails strategies such as improving classification algorithms or balancing classes in the training data (data preprocessing) before providing the data as input to the machine learning algorithm.

Non Fraudulent Observations after random under sampling = 10 % of 980 =98 Total Observations after combining them with Fraudulent observations = 20+98=118 Event Rate for the new dataset after under sampling = 20/118 = 17%

Non Fraudulent Observations =980 Fraudulent Observations after replicating the minority class observations= 400 Total Observations in the new data set after oversampling=1380 Event Rate for the new data set after under sampling= 400/1380 = 29 %  

sample of 15 instances is taken from the minority class and similar synthetic instances are generated 20 times Post generation of synthetic instances, the following data set is created Minority Class (Fraudulent Observations) = 300 Majority Class (Non-Fraudulent Observations) = 980 Event rate= 300/1280 = 23.4 %

The algorithm randomly selects a data point from the k nearest neighbors for the security sample, selects the nearest neighbor from the border samples and does nothing for latent noise.

                                     Figure 4:  Approach to Bagging Methodology Total Observations = 1000 Fraudulent   Observations =20 Non Fraudulent Observations = 980 Event Rate= 2 % There are 10 bootstrapped samples chosen from the population with replacement.

The machine learning algorithms like logistic regression, neural networks, decision tree  are fitted to each bootstrapped sample of 200 observations.

And the Classifiers c1, c2…c10 are aggregated to produce a compound classifier.  This ensemble methodology produces a stronger compound classifier since it combines the results of individual classifiers to come up with an improved one.

Ada Boost is the first original boosting technique which creates a highly accurate prediction rule by combining many weak and inaccurate rules.  Each classifier is serially trained with the goal of correctly classifying examples in every round that were incorrectly classified in the previous round.

For a learned classifier to make strong predictions it should follow the following three conditions: Each of the weak hypothesis has an accuracy slightly better than random guessing i.e.

This is the fundamental assumption of this boosting algorithm which can produce a final hypothesis with a small error After each round, it gives more focus to examples that are harder to classify.  The quantity of focus is measured by a weight, which initially is equal for all instances.

     Figure 7:  Approach to Gradient Boosting For example: In a training data set containing 1000 observations out of which 20 are labelled fraudulent an initial base classifier.

A differentiable loss function is calculated based on the difference between the actual output and the predicted output of this step.  The residual of the loss function is the target variable (F1) for the next iteration.

The data structure  of the rare event data set is shown below post missing value removal, outlier treatment and dimension reduction.

Results This approach of balancing the data set with SMOTE and training a gradient boosting algorithm on the balanced set significantly impacts the accuracy of the predictive model.

By increasing its lift by around 20% and precision/hit ratio by 3-4 times as compared to normal analytical modeling techniques like logistic regression and decision trees.

She has around 3.5 + years of work experience and has worked in multiple advanced analytics and data science engagements spanning industries like Telecom, utilities, banking , manufacturing.

Machine Learning with Python: Easy and robust method to fit nonlinear data

Fortunately, scikit-learn, the awesome machine learning library, offers ready-made classes/objects to answer all of the above questions in an easy and robust way.

Here is a simple video of the overview of linear regression using scikit-learn and here is a nice Medium article for your review.

This is essential for any machine learning task, so that we don’t create model with all of our data and think the model is highly accurate (because it has ‘seen’ all the data and fitted nicely) but it performs badly when confronted with new (‘unseen’) data in the real world.

Automatic polynomial feature generation: Scikit-learn offers a neat way to generate polynomial features from a set of linear features.

In a linear regression setting, the basic idea is to penalize the model coefficients such that they don’t grow too big and overfit the data i.e.

In its most common form, it consists of data generation/ingestion, data cleaning and transformation, model(s) fitting, cross-validation, model accuracy testing, and final deployment.

Scikit-learn offers a pipeline feature which can stack multiple models and data pre-processing classes together and turn your raw data into usable models.

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