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Today's Interview QnAs

Company Name - Bridgei2i Analytics

Role - Data Scientist


Q. Why does overfitting occur?

A. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model


Q. What is ensemble learning?

A. Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one.


Q. What is F1 score?

A. The F1 score is defined as the harmonic mean of precision and recall. As a short reminder, the harmonic mean is an alternative metric for the more common arithmetic mean. It is often useful when computing an average rate. In the F1 score, we compute the average of precision and recall.


Q. What is pickling and unpickling?

A.“Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy.


Q. What is lambda function?

A. Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python.


Q. What is the trade of between bias and variance ?

A. Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance.

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Today's Interview QnAs

Company Name - PWC

Role - Data Scientist

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Q. What is feature scaling and why is it necessary?

A. Feature scaling in machine learning is one of the most critical steps during the pre-processing of data before creating a machine learning model. Scaling can make a difference between a weak machine learning model and a better one.

The most common techniques of feature scaling are Normalization and Standardization.


Q. Difference between likelihood and probability?

A. Probability basically corresponds to finding the chance of something given a sample distribution of the data, while on the other hand, likelihood refers to finding the best distribution of the data given a particular value of some feature or some situation in the data.

While calculating the probability, feature value can be varied, but the characteristics(mean, standard and deviation) of the data distribution cannot be altered.

The likelihood in very simple terms means to increase the chances of a particular situtation to happen by varrying the characteristics of the dataset distribution.


Q. Difference between Sigmoid and Softmax function?

A. The Sigmoid Activation Function is a mathematical function with a recognizable “S” shaped curve. It is used for the logistic regression and basic neural network implementation. If we want to have a classifier to solve a problem with more than one right answer, the Sigmoid Function is the right choice. We should apply this function to each element of the raw output independently. The return value of Sigmoid Function is mostly in the range of values between 0 and 1 or -1 and 1.

Whereas the Softmax Activation Function, also know as SoftArgMax or Normalized Exponential Function is a fascinating activation function that takes vectors of real numbers as inputs, and normalizes them into a probability distribution proportional to the exponentials of the input numbers. Before applying, some input data could be negative or greater than 1. Also, they might not sum up to 1. After applying Softmax, each element will be in the range of 0 to 1, and the elements will add up to 1. This way, they can be interpreted as a probability distribution. For more clarification, the larger the input number, the larger the probabilities will be


Q. Which machine learning algorithm is known as the lazy learner ?

A. KNN is a Machine Learning algorithm known as a lazy learner. K-NN is a lazy learner because it doesn’t learn any machine learnt values or variables from the training data but dynamically calculates distance every time it wants to classify, hence memorises the training dataset instead.

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Today's Interview QnAs

Company Name - IBM

Role - Data Scientist



Q1. What is P-value?

Ans. The p-value is the probability that the null hypothesis is true. (1 – the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.


Q2. Forward and Backward selection? It's working?

Ans. Forward Selection chooses a subset of the predictor variables for the final model. Unlike forward stepwise selection, it begins with the full least squares model containing all p predictors, and then iteratively removes the least useful predictor, one-at-a-time. forward selection starts with a null model (with no predictors) and proceeds to add variables one at a time, and so unlike backward selection, it DOES NOT have to consider the full model (which includes all the predictors).


Q3. Fix multicollinearity?

Ans. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove those features from our data.


Q4. Difference between Gradient boosting and random forest?

Ans. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.


Q5. Precision and Recall? How they are related to ROC curve?

Ans. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. The precision is the proportion of relevant results in the list of all returned search results. When dealing with highly skewed datasets, Precision-Recall (PR) curves in give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space.



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Today's Interview Quest

Company Name - Cognizant
Role - Data Scientist

Q. Describe how Gradient Boosting works.

A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.


Q. Describe the decision tree model.

A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.


Q. What is a neural network?

A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.


Q. Explain the Bias-Variance Tradeoff

A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.


Q. What’s the difference between L1 and L2 regularization?

A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.

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Today's Interview QnAs

Company Name - JP Morgan

Role: Data Scientist


Q. Explain How a System Can Play a Game of Chess Using Reinforcement Learning?

A. The chess-playing reinforcement learning model starts with a clean slate, and is only given the basic rules of moving the pieces and the ultimate goal, which is to drive the opponent into check mate. At the beginning, the AI knows nothing about the tactics of the game and makes random moves.
But after playing against itself thousands and millions of times, it starts to develop a statistical model of what sequences of moves are likely to win each situation.

With reinforced learning, It will make a move (decision), check if it’s the right move (feedback), and keep the outcomes in memory for the next step it takes (learning). There is a reward for every correct decision the system takes and punishment for the wrong one.


Q. Difference between Gini Impurity and Entropy in a Decision Tree?

A. The criteria typically used to decide which feature to split on are the Gini index and information entropy. Both of these measures are pretty similar numerically.

*The Gini index is used by the CART (classification and regression tree) algorithm, whereas information gain via entropy reduction is used by algorithms like C4.5.
*Entropy takes slightly more computation time than Gini Index because of the log calculation, maybe that's why Gini Index has become the default option for many ML algorithms. 
*The range of Entropy lies in between 0 to 1 and the range of Gini Impurity lies in between 0 to 0.5. 
*Gini measurement is the probability of a random sample being classified incorrectly if we randomly pick a label according to the distribution in a branch.Whereas entropy is a measure of information that indicates the disorder of the features with the target.



Q. Suppose you found that your model is suffering from low bias and high variance. How would you tackle it?

A. Low bias and High variance is the classic term of overfitting which means your model is performing very good in the training dataset, but not good in the test data or unseen data
regularization, creating a random forest or any bagging technique work as well. Rationale of bagging is as simple as following - if X1 , ..., Xn are independently distributed with variance v , variance of their average becomes v/n and thereby reducing the variance.



Q. Difference between stochastic gradient descent (SGD) and gradient descent (GD)?

A. In both gradient descent (GD) and stochastic gradient descent (SGD), you update a set of parameters in an iterative manner to minimize an error function.

While in GD, you have to run through ALL the samples in your training set to do a single update for a parameter in a particular iteration, in SGD, on the other hand, you use ONLY ONE or SUBSET of training sample from your training set to do the update for a parameter in a particular iteration. If you use SUBSET, it is called Minibatch Stochastic gradient Descent.

Thus, if the number of training samples are large, in fact very large, then using gradient descent may take too long because in every iteration when you are updating the values of the parameters, you are running through the complete training set. On the other hand, using SGD will be faster because you use only one training sample and it starts improving itself right away from the first sample.

SGD often converges much faster compared to GD but the error function is not as well minimized as in the case of GD. Often in most cases, the close approximation that you get in SGD for the parameter values are enough because they reach the optimal values and keep oscillating there.


Q. What are crosstab in python?

A. Crosstab function builds a cross-tabulation table that can show the frequency with which certain groups of data appear.


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Today's Interview QnAs

Company Name - Honeywell

Role: Data Scientist


Q. Significance of Gamma and Regularization in SVM?


A. Gamma and C(behaves as a regularization parameter in svm) are the two parameters which is very important in SVM. The behavior of the model is very sensitive to the gamma parameter. If gamma is too large, the radius of the area of influence of the support vectors only includes the support vector itself and no amount of regularization with C will be able to prevent overfitting. When gamma is very small, the model is too constrained and cannot capture the complexity or “shape” of the data. The resulting model will behave similarly to a linear model with a set of hyperplanes that separate the centers of high density of any pair of two classes.



Q. Is ARIMA model a good fit for every time series problem?


A. No we cannot use it for every problem although it's a popular method in time series. ARIMA models are applied in some cases where data show evidence of non-stationarity in the sense of mean (but not variance/autocovariance), where an initial differencing step (corresponding to the "integrated" part of the model) can be applied one or more times to eliminate the non-stationarity of the mean function.




Q. Difference between stochastic gradient descent (SGD) and gradient descent (GD)?


A. In both gradient descent (GD) and stochastic gradient descent (SGD), you update a set of parameters in an iterative manner to minimize an error function.

While in GD, you have to run through ALL the samples in your training set to do a single update for a parameter in a particular iteration, in SGD, on the other hand, you use ONLY ONE or SUBSET of training sample from your training set to do the update for a parameter in a particular iteration. If you use SUBSET, it is called Minibatch Stochastic gradient Descent.

Thus, if the number of training samples are large, in fact very large, then using gradient descent may take too long because in every iteration when you are updating the values of the parameters, you are running through the complete training set. On the other hand, using SGD will be faster because you use only one training sample and it starts improving itself right away from the first sample.

SGD often converges much faster compared to GD but the error function is not as well minimized as in the case of GD. Often in most cases, the close approximation that you get in SGD for the parameter values are enough because they reach the optimal values and keep oscillating there.



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