Today's Interview QnAs

Company - Hitachi

Role: Data Scientist


1. What is the difference between supervised learning and unsupervised learning? Give concrete examples.

Supervised learning involves learning a function that maps an input to an output.
For example, if I had a dataset with two variables, age (input) and height (output), I could implement a supervised learning model to predict the height of a person based on their age.

Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. A common use of unsupervised learning is grouping customers by purchasing behavior to find target markets.


2.How do you assess the statistical significance of an insight?

Ans: You would perform hypothesis testing to determine statistical significance. First, you would state the null hypothesis and alternative hypothesis.
Second, you would calculate the p-value, the probability of obtaining the observed results of a test assuming that the null hypothesis is true. Last, you would set the level of the significance (alpha) and if the p-value is less than the alpha, you would reject the null — in other words, the result is statistically significant.


3. What is the Law of Large Numbers?

Ans: The Law of Large Numbers is a theory that states that as the number of trials increases, the average of the result will become closer to the expected value.
Eg. flipping heads from fair coin 100,000 times should be closer to 0.5 than 100 times.

4.If a Company says that they want to double the number of ads in Newsfeed, how would you figure out if this is a good idea or not?

Ans: You can perform an A/B test by splitting the users into two groups: a control group with the normal number of ads and a test group with double the number of ads. Then you would choose the metric to define what a “good idea” is. For example, we can say that the null hypothesis is that doubling the number of ads will reduce the time spent on Facebook and the alternative hypothesis is that doubling the number of ads won’t have any impact on the time spent on Facebook. However, you can choose a different metric like the number of active users or the churn rate. Then you would conduct the test and determine the statistical significance of the test to reject or not reject the null.

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Today's Interview QnAs

Company: Bajaj Finserve

Role: Data Scientist


1. How is random forest better then decision tree?

Ans: Random forest is an ensemble method that takes many weak decision trees to make a strong learner. Random forests are more accurate, more robust, and less prone to overfitting.


2. Explain what the bootstrap sampling method is and give an example of when it’s used.

Ans: Bootstrap sampling method is a resampling method that uses random sampling with replacement.

It’s an essential part of the random forest algorithm, as well as other ensemble learning algorithms.


3. What happens if the learning rate is set too high or too low?

Ans: If the learning rate is too low, your model will train very slowly as minimal updates are made to the weights through each iteration. Thus, it would take many updates before reaching the minimum point.

If the learning rate is set too high, this causes undesirable divergent behavior to the loss function due to drastic updates in weights, and it may fail to converge.

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Today's Interview QnAs

Company : Idexcel

Role: Data Scientist


1. What is the interquartile range (IQR)?

The IQR is the difference between the third quartile (Q3) and the first quartile (Q1) of a set of data. Specifically, IQR = Q3–Q1.


2.What is the difference between a univariate analysis and a bivariate analysis?

The univariate analysis involves analyzing the distribution of a single variable. Bivariate analysis, on the other hand, considers the relationship between two distinct variables.
For example, calculating the average amount of coffee consumed by a certain population would be a univariate analysis. In contrast, understanding the relationship between coffee consumption and age, gender, or time of the year would be examples of bivariate analysis.


3.What is the difference between the central limit theorem and the law of large numbers?

The central limit theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the shape of the original distribution.
The law of large numbers states that as the number of trials increases, the average of the results will become closer to the expected value. For example, the proportion of heads in 1,000 fair coin tosses is closer to 0.5 than it is in 10 tosses.


4.Does feature scaling always result in improvement in model accuracy?

Feature scaling is not necessary when using tree-based algorithms e.g. decision trees, random forest, and gradient boosting as they are invariant to the scale of the features. Decision trees split each node using one feature at a time and as a result, they are unaffected by the other features in the dataset.
For gradient descent and distance-based algorithms, however, feature scaling can result in improvement in model accuracy.

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Today's Interview QnAs

Company Name: Memphis

Role: Data Scientist


1. Should Outliers be treated for Logistic regression?

Ans: Logistics regression uses Sigmoid function to sqashing the Best fit line which restricts the value of yi or the target variable between 0 and 1. Hence it's not much impacted by outliers.
However when there is huge number of outliers so it will affect my logistics regression as well as then my Sigmoid function will fit on dataset in such a way that it would try to compensate for the maximum outliers thus affecting the best fit line.


2. How would you choose the best feature for the root node as per the entropy criterion?

Ans: We will always choose that feature which gives us the maximum information gain once it splits. Information gain is the difference of entropy of the feature as root node and the summation of the entropy of the child nodes


3. Why do we need to have regularisation in Logistics Regression?

Ans: Since the optimization function, which has weight vectors , features and target variables, can only get minimum value of zero when weight vectors will tend to infinity. This will be a perfect case of overfitting since my optimization function goes to the minimum possible value which is zero. Hence we need to use regularisation to avoid overfitting.


4. What are independent events?

Ans: In probability, we say two events are independent if knowing one event occurred doesn't change the probability of the other event. For example, the probability that a fair coin shows "heads" after being flipped is 1 / 2.

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Today's Interview QnAs

Company Name - Zensar

Role: Data Scientist


1. What are the assumptions required for linear regression? What if some of these assumptions are violated?

Ans: The assumptions are as follows:

The sample data used to fit the model is representative of the population

The relationship between X and the mean of Y is linear

The variance of the residual is the same for any value of X (homoscedasticity)

Observations are independent of each other

For any value of X, Y is normally distributed.

Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate.


2.What is multicollinearity and how to remove it?

Ans: Multicollinearity exists when an independent variable is highly correlated with another independent variable in a multiple regression equation. This can be problematic because it undermines the statistical significance of an independent variable.

You could use the Variance Inflation Factors (VIF) to determine if there is any multicollinearity between independent variables — a standard benchmark is that if the VIF is greater than 5 then multicollinearity exists.


3. What is overfitting and how to prevent it?

Ans: Overfitting is an error where the model ‘fits’ the data too well, resulting in a model with high variance and low bias. As a consequence, an overfit model will inaccurately predict new data points even though it has a high accuracy on the training data.

Few approaches to prevent overfitting are:

- Cross-Validation:Cross-validation is a powerful preventative measure against overfitting. Here we use our initial training data to generate multiple mini train-test splits. Now we use these splits to tune our model.

- Train with more data: It won’t work every time, but training with more data can help algorithms detect the signal better or it can help my model to understand general trends in particular.

- We can remove irrelevant information or the noise from our dataset.

- Early Stopping: When you’re training a learning algorithm iteratively, you can measure how well each iteration of the model performs.

Up until a certain number of iterations, new iterations improve the model. After that point, however, the model’s ability to generalize can weaken as it begins to overfit the training data.

Early stopping refers stopping the training process before the learner passes that point.

- Regularization: It refers to a broad range of techniques for artificially forcing your model to be simpler. There are mainly 3 types of Regularization techniques:L1, L2,&,Elastic- net.

- Ensembling : Here we take number of learners and using these we get strong model. They are of two types : Bagging and Boosting.


4. Given two fair dices, what is the probability of getting scores that sum to 4 and 8?

Ans: There are 4 combinations of rolling a 4 (1+3, 3+1, 2+2):
P(rolling a 4) = 3/36 = 1/12

There are 5 combinations of rolling an 8 (2+6, 6+2, 3+5, 5+3, 4+4):
P(rolling an 8) = 5/36

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Today's Interview QnAs

Company: Celstream Technologies

Role: Data Scientist


1. Why is KNN algorithm called Lazy Learner?

Ans: When it gets the training data, it does not learn and make a model, it just stores the data. It does not derive any discriminative function from the training data. It uses the training data when it actually needs to do some prediction. So, KNN does not immediately learn a model, but delays the learning, that is why it is called lazy learner.


2. What is stratify in Train_test_split?

Ans: Stratification means that the train_test_split method returns training and test subsets that have the same proportions of class labels as the input dataset. So if my input data has 60% 0's and 40% 1's as my class label, then my train and test dataset will also have similar proportions.



3. Explain the difference between lists and tuples.

Ans: Both lists and tuples are made up of elements, which are values of any Python data type.

Lists are mutable, while tuples are immutable.

Lists are created using Square brackets (for example, my_list = [a, b, c]), while tuples are in parentheses (for example, my_tuple = (a, b, c)).


4. What is the difference between classification and regression?

Ans: Classification is about predicting a label and regression is about predicting a quantity. In classification we predict a discrete class label . Examples of Classification algorithm are Decision Tree, KNN, Naive Bayes, Logistic regression etc.

In regression we predict a continuous quantity output. Examples of regression models are Linear regression, Random Forest regressor etc.

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Today's Interview QnAs

Company: TCS

Role: Data Scientist


1. What is Information gain?

Ans: Information gain is the difference of entropy of the parent node and the weighted entropies of the child nodes.

Information Gain = Entropy before splitting - Entropy after splitting

Suppose we have multi features in a dataset, so we will choose that feature as a root node which will have maximum Information Gain.


Information gain is used for determining the best features/attributes that render maximum information about a class. It follows the concept of entropy while aiming at decreasing the level of entropy, beginning from the root node to the leaf nodes.



2. When accuracy should not be used as a parameter to measure the performance of Classification model?

Ans: There are 2 major situations when you will not use accuracy as a parameter for classification performance measures-

A) When you have severely imbalanced dataset. If suppose you have a dataset with 90% as positive class and 10% as negative class, then even a dumb model can classify all the datapoints as positive class and attain the accuracy as 90%, which is not sensible.

B) Since accuracy does not considers probability values so if I have a threshold of 0.5 and my model M1 gives the probability value for a "x" as 0.9 and my second model gives the probability value of 0.55, both of them will be labelled as positive class but we know the performance of M1 is much better than M2 when you see the actual probabilities.


3. Why do we use the summary function?

Ans: Summary functions are used to give the summary of all the numeric values in a dataframe. Eg. The describe() function can be used to provide the summary of all the data values given to it.

column_name.describe() will give the following values of all the numeric data in the column-

Count
Mean
Std-Standard deviation
Min-Minimum
25%
50%
75%
max-Maximum


4. How are Entropy and Gini index related?

Ans: Both Entropy and Gini index are parabolic in nature when it's plotted by the probability. However the maximum value of my Gini Impurity is 0.5 while that of entropy is 1 which only occurs when my classes are Equi-probable. Both of them will tend to zero values when one of the classes are dominant.

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Today's Interview QnAs

Company: Splunk

Role: Data Scientist


1. Explain ways to train hyperparameters in a neural network?

Ans:Hyperparameters in a neural network can be trained using four components:

1. Batch size: Indicates size of input data

2. Epochs: Denotes the number of times the training data is visible to neural network to train

3. Momentum: Used to get an idea of next steps that occur with data being executed

4. Learning rate: Represents time required for network to update parameters and learn


2. What happens if the learning rate is set too high or too low?

Ans: If the learning rate is too low, your model will train very slowly as minimal updates are made to the weights through each iteration. Thus, it would take many updates before reaching the minimum point.
If the learning rate is set too high, this causes undesirable divergent behavior to the loss function due to drastic updates in weights, and it may fail to converge.


3. Explain Crude working of LSTM? ==> How Does an LSTM Network Work?

Ans: Long-Short-Term Memory (LSTM) is a special kind of recurrent neural network RNN capable of learning long-term dependencies, remembering information for long periods as its default behaviour. Crude steps in an LSTM network are as:

1: Network decides what to forget and what to remember
2: It selectively updates cell state values
3: Network decides what part of current state makes it to output


4. Explain what is Swish Function?

Ans: Swish is an activation function proposed by Google which is an alternative to ReLU activation function.

It is represented as: f(x) = x * sigmoid(x)

Swish function works better than ReLU for a variety of deeper models.

The derivative of Swist can be written as: y’ = y + sigmoid(x) * (1 - y)

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Today's Interview QnAs

Company: The Math Company

Role: Data Scientist


1. What are Different Kernels in SVM?

Ans: There are six types of kernels in SVM:
Linear kernel - used when data is linearly separable.
Polynomial kernel - When you have discrete data that has no natural notion of smoothness.
Radial basis kernel - Create a decision boundary able to do a much better job of separating two classes than the linear kernel.
Sigmoid kernel - used as an activation function for neural networks.


2. According to you what are some advantages in using a CNN over a DNN (dense neural network) for an image classification task?

Ans: Both models can capture relationship between close pixels but CNNs have some useful properties like: translation invariant — exact location of pixel is irrelevant for filter less likely to overfit — typical number of parameters in a CNN is much smaller than that of a DNN gives better understanding of model — one can look at filters, weights and visualize what network learned hierarchical nature — learns patterns in by describing complex patterns using simpler ones.


3. What are your intuitions about pruning in decision tree?

Ans: Pruning a decision tree helps to prevent overfitting the training data so that our model generalizes well to unseen data. Pruning a decision tree means to remove a subtree that is redundant and not a useful split and replace it with a leaf node. Decision tree pruning can be divided into two types: pre-pruning and post-pruning.

Pre-pruning, also known as Early Stopping Rule, is the method where the subtree construction is halted at a particular node after evaluation of some measure. These measures can be the Gini Impurity or the Information Gain. In pre-pruning, we evaluate the pruning condition based on the above measures at each node.
post-pruning means to prune after the tree is built. You grow the tree entirely using your decision tree algorithm and then you prune the subtrees in the tree in a bottom-up fashion. You start from the bottom decision node and, based on measures such as Gini Impurity or Information Gain, you decide whether to keep this decision node or replace it with a leaf node.



4. Give some intuition about Epoch, Batch, and Iteration in deep learning how they are different?

Ans:
1. Epoch - Represents one iteration over entire dataset (everything put into training model)
2. Batch - Refers to when we cannot pass entire dataset into neural network at once, so we divide dataset into several batches
3. Iteration - if we have 100,000 images as data and a batch size of 400. then an epoch should run 250 iterations (100,000 divided by 400)

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Today's Interview QnAs

Company: Syngenta

Role: Data Scientist


1. What are Gaussian Mixture Model? Where they are used?

Probabilistic model for representing normally distributed subpopulations within an overall population. Mixture models in general don't require knowing which subpopulation a data point belongs to, allowing the model to learn the subpopulations automatically. Since subpopulation assignment is not known, this constitutes a form of unsupervised learning. These models have been used for feature extraction from speech data, and have also been used extensively in object tracking of multiple objects, where number of mixture components and their means predict object locations at each frame in a video sequence. change in place of 1st.


2. What are Hard-Margin and Soft-Margin SVMs?

Hard-Margin SVMs have linearly separable training data. No data points are allowed in the margin areas. This type of linear classification is known as Hard margin classification.
Soft-Margin SVMs have training data that are not linearly separable. Margin violation means choosing a hyperplane, which can allow some data points to stay either in between the margin area or on the incorrect side of the hyperplane.


3. What is the empirical rule?

In statistics, the empirical rule states that every piece of data in a normal distribution lies within three standard deviations of the mean. It is also known as the 68–95–99.7 rule. According to the empirical rule, the percentage of values that lie in a normal distribution follow the 68%, 95%, and 99.7% rule. In other words, 68% of values will fall within one standard deviation of the mean, 95% will fall within two standard deviations, and 99.75 will fall within three standard deviations of the mean.


4. What is the left-skewed distribution and the right-skewed distribution?

In the left-skewed distribution, the left tail is longer than the right side.

Mean < median < mode

In the right-skewed distribution, the right tail is longer. It is also known as positive-skew distribution.

Mode < median < mean

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