Today's Interview QnAs

Company Name - Genpact

Role: Data Scientist



Q. How will you measure the Euclidean distance between the two arrays in NumPy?

A. The magnitude or length of the line segment between two locations is known as the Euclidean distance in mathematics. We first create two numpy arrays in the first procedure. The Euclidean distance is then computed directly using numpy's linalg.norm() function.



Q. Difference between Normalisation and Standardization?

A. Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance).



Q. What is Ensemble Learning?

A. Ensemble learning is the process of systematically generating and combining many models, such as classifiers or experts, to tackle a specific computational intelligence problem. Ensemble learning is largely used to improve a model's performance (classification, prediction, function approximation, etc.) or to lessen the risk of an unintentional poor model selection.



Q. Difference between Batch and Stochastic Gradient Descent?

A. Batch Gradient Descent is highly slow on very big training sets since it entails calculations over the entire training set at each step. As a result, Batch GD becomes extremely computationally expensive. SGD is stochastic in nature, which means it chooses up a "random" instance of training data at each step and then computes the gradient, which is significantly faster than Batch GD because there is much less data to modify at once.


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Today's Interview QnAs

Company Name - Accenture

Role: Data Scientist


Q. Why do we use Cross validation?

A. Cross-validation is a technique used in applied machine learning to estimate a machine learning model's skill on unknown data. That is, to use a small sample to assess how the model will perform in general when used to generate predictions on data that was not utilised during the model's training.



Q. How will you work in a machine learning project if there is a huge imbalance in the data?

A. Machine Learning algorithms tend to produce unsatisfactory classifiers when faced with imbalanced datasets. To overcome this, methods like randome oversampling and random undersampling can be used. Synthetic Minority Over-sampling Technique, Modified synthetic minority oversampling technique (MSMOTE), Algorithmic Ensemble Techniques can also be used.



Q. Formula of sigmoid function?

A. A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. Sigmoid functions most often show a return value (y axis) in the range 0 to 1. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula

S(x) = 1/(1+exp(-x))



Q. Can we use sigmoid function in case of multiple classification?

A. You must use Softmax if your task is a type of classification where the labels are mutually exclusive and each input only has one label. If you have numerous labels for each input in your classification work, your classes are not mutually exclusive, and you can utilise Sigmoid for each output. In the first scenario, the output should be the output entry with the highest value. In the latter scenario, you have an activation value for each class that belongs to the last sigmoid. You can assert that an entry exists in the input if each activation is greater than 0.5.

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Today's Interview QnAs

Company Name - Mindtree

Role: Data Scientist



Q. What is central tendency ?

A. The central tendency is a descriptive description of a dataset represented by a single value that represents the data distribution's center. It does not provide information on individual values in the dataset, but it does provide a thorough overview of the entire dataset. The following measurements can be used to describe the central tendency of a dataset in general: Mean: Represents the sum of all values in a dataset divided by total number of values in the dataset. The median is the midpoint value in an ascending-ordered dataset. The most often occurring value in a dataset is defined by the mode.




Q. Which central tendency method is used If there exists any outliers ?

A. Because the value of the mean might be skewed by outliers, the median is frequently recommended in these instances. It will, however, depend on how powerful the outliers are. Using the mean as the measure of central tendency is usually preferred if they do not significantly alter the mean.



Q. Explain Central limit theorem ?

A. The central limit theorem (CLT) asserts that as the sample size grows higher, the distribution of a sample variable approaches a normal distribution (i.e., a "bell curve"), assuming that all samples are identical in size and independent of the population's actual distribution shape.



Q. What is Chi-Square test ?

A. A hypothesis testing method is the Chi-square test. Checking if observed frequencies in one or more categories match expected frequencies is one of two frequent Chi-square tests. The Chi-square test is used to determine whether your data is as expected. The test's core premise is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. The Chi-square goodness of fit test and the Chi-square test of independence are two regularly used Chi-square tests. Both tests use variables to categorize your data into groups.

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Today's Interview QnAs

Company Name - Cognizant

Role: Data Scientist



Q. When underfitting occurs in a static model?

A. When a mathematical model or a machine learning algorithm fails to capture the primary trend of the data, this is known as underfitting. When a model is too simplistic — informed by too few features or overly regularised — it becomes inflexible in learning from the dataset, resulting in underfitting.


Q. While working on a data set,how can you select important variables?

A. 1. Variance: You can remove characteristics with low variance because they don't provide any insight into the forecast and overfit the model. 2. Correlated Features: There may be features with significant correlations among them; we can keep a few and discard the rest. 3. From the Model: This approach of feature selection takes longer but provides us with the most important features supplied by the models themselves. In this case, we can employ 2-3 models and place the train set on top of them, instructing the model to output a certain amount of relevant functions while ignoring the rest. In the Sklearn RFE and RFECV feature selection modules, there are two functions that we can use to pick features.


Q. What is Autoencoder?

A. Autoencoder is an unsupervised artificial neural network that learns how to compress and encode data effectively before reconstructing it back to a representation that is as similar to the original input as feasible. By definition, an autoencoder decreases data dimensionality by learning to ignore noise in the data. Encoder, Bottleneck, Decoder, and Reconstruction Loss: Autoencoders are made up of four primary parts: encoder, bottleneck, decoder, and reconstruction loss.



Q. Difference between violin plot and box plot?

A. Although violin plots are similar to box plots, they provide additional information such as the sample data distribution (density trace). Box plots show data points outside 1.5 times the inter-quartile range as outliers above or below the whiskers by default, whereas violin plots show the entire range of the data.


Q. What is statistical power of sensitivity and how do you calculate it?

A. The statistical power of an A/B test refers to the test's sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (α) if a true effect of a certain magnitude (MEI) is in fact present.

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