One-class SVM is a variation of the SVM that can be used in an unsupervised setting for anomaly detection. Let’s say we are analyzing credit card transactions to identify fraud. We are likely to have many normal transactions and very few fraudulent transactions. Also, the next fraud transaction might be completely different from all previous…

# Category: Machine Learning

## Can we use the AUC Metric for a SVM Classifier ?

What is AUC ? AUC is the area under the ROC curve. It is a popularly used classification metric. Classifiers such as logistic regression and naive bayes predict class probabilities as the outcome instead of the predicting the labels themselves. A new data point is classified as positive if the predicted probability of positive class…

## What is the difference between supervised and unsupervised learning ?

In Supervised Learning the algorithm learns from labeled training data. In other words, each data point is tagged with the answer or the label the algorithm should come up with. Using such labeled data, the goal is to predict labels for new data points. The two common forms of supervised learning are classification and regression….

## When are deep learning algorithms more appropriate compared to traditional machine learning algorithms?

Deep learning algorithms are capable of learning arbitrarily complex non-linear functions by using a deep enough and a wide enough network with the appropriate non-linear activation function. Traditional ML algorithms often require feature engineering of finding the subset of meaningful features to use. Deep learning algorithms often avoid the need for the feature engineering step….

## What is overfitting and underfitting ? Give examples. How do you overcome them?

ANSWER here

## Why do you typically see overflow and underflow when implementing an ML algorithms ?

A common pre-processing step is to normalize/rescale inputs so that they are not too high or low. However, even on normalized inputs, overflows and underflows can occur: Underflow: Joint probability distribution often involves multiplying small individual probabilities. Many probabilistic algorithms involve multiplying probabilities of individual data points that leads to underflow. Example : Suppose you…

## Is the run-time of an ML algorithm important? How do I evaluate whether the run-time is OK?

Runtime considerations are often important for many applications. Typically you should look at training time and prediction time for an ML algorithm. Some common questions to ask include: Training: Do you want to train the algorithm in a batch mode? How often do you need to train? If you need to retrain your algorithm every…

## How do you handle missing data in an ML algorithm ?

Missing data is caused either due to issues in data collection or sometimes, the data model could allow for missing data (for instance, the field ‘maximum credit limit on any of your cards’ might not make sense for someone who has no credit cards…). With missing data, typically the ML algorithm implementation might fail with…

## With the maximum likelihood estimate are we guaranteed to find a global Optima ?

Maximum likelihood estimate finds that value of parameters that maximize the likelihood. If the likelihood is strictly concave(or negative of likelihood is strictly convex), we are guaranteed to find a unique optimum. This is usually not the case and we end up finding a local optima. Hence, the Maximum likelihood estimate usually finds a local…

## What is the difference between deep learning and machine learning?

Deep learning is a subset of Machine Learning. Machine learning is the ability to build “models” that can learn automatically from data, without programming explicit rules. Machine Learning models typically have the ability to generalize to new data. Deep Learning is a field in machine learning where we build multi-layered artificial neural network models to…