Tools to crack your data science Interviews
Simpsons Paradox occures when trends in aggregates are reversed when examining trends in subgroups. Data often has biases that are might might lead to unexpected trends, but digging deeper and deciphering these biases and looking at appropriate sub-groups leads to drawing the right insights. Why does Simpson’s paradox occur ? Arithmetically, when (a1/A1) < (a2/A2)…
Most of us know that ML models often tend to overfit to the training data for various reasons. This could be due to lack of enough training data or the training data not being representative of data we expect to apply the model on. But the result is that we end up building an overly…
All of us know random forests, one of the most popular ML models. They are a supervised learning algorithm, used in a wide variety of applications for classification and regression. Can we use random forests in an unsupervised setting? (where we have no labeled data?) Isolation forests are a variation of random forests that can…
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…
This video explains computing the AUC metric for an SVM classifier, or other classifiers that give the absolute class values as outcomes. What is Area Under the Curve ? AUC is the area under the ROC curve. It is a popularly used classification metric. If you want to recap how AUC works, here is a…
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….
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….
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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…
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…