**What is Interpolation and Extrapolation?**

The terms of interpolation and extrapolation are extremely important in any statistical analysis. Extrapolation is the determination or estimation using a known set of values or facts by extending it and taking it to an area or region that is unknown. It is the technique of inferring something using data that is available.

Interpolation on the other hand is the method of determining a certain value which falls between a certain set of values or the sequence of values. This is especially useful when you have data at the two extremities of a certain region but you don’t have enough data points at the specific point. This is when you deploy interpolation to determine the value that you need.

**What is Power Analysis?**

The power analysis is a vital part of the experimental design. It is involved with the process of determining the sample size needed for detecting an effect of a given size from a cause with a certain degree of assurance. It lets you deploy specific probability in a sample size constraint.

The various techniques of statistical power analysis and sample size estimation are widely deployed for making statistical judgment that are accurate and evaluate the size needed for experimental effects in practice.

Power analysis lets you understand the sample size estimate so that they are neither high nor low. A low sample size there will be no authentication to provide reliable answers and if it is large there will be wastage of resources.

**What is K-means? How can you select K for K-means?**

K-means clustering can be termed as the basic unsupervised learning algorithm. It is the method of classifying data using a certain set of clusters called as K clusters. It is deployed for grouping data in order to find similarity in the data.

It includes defining the K centers, one each in a cluster. The clusters are defined into K groups with K being predefined. The K points are selected at random as cluster centers. The objects are assigned to their nearest cluster center. The objects within a cluster are as closely related to one another as possible and differ as much as possible to the objects in other clusters. K-means clustering works very well for large sets of data.

TIBCO Software Most Frequently Asked Data Science Interview Questions Answers |

**How is Data modeling different from Database design?**

Data Modeling: It can be considered as the first step towards the design of a database. Data modeling creates a conceptual model based on the relationship between various data models. The process involves moving from the conceptual stage to the logical model to the physical schema. It involves the systematic method of applying the data modeling techniques.

Database Design: This is the process of designing the database. The database design creates an output which is a detailed data model of the database. Strictly speaking database design includes the detailed logical model of a database but it can also include physical design choices and storage parameters.

**You are given a train data set having 1000 columns and 1 million rows. The data set is based on a classification problem. Your manager has asked you to reduce the dimension of this data so that model computation time can be reduced. Your machine has memory constraints. What would you do? (You are free to make practical assumptions.)?**

Processing a high dimensional data on a limited memory machine is a strenuous task, your interviewer would be fully aware of that. Following are the methods you can use to tackle such situation:

Since we have lower RAM, we should close all other applications in our machine, including the web browser, so that most of the memory can be put to use.

We can randomly sample the data set. This means, we can create a smaller data set, let’s say, having 1000 variables and 300000 rows and do the computations.

To reduce dimensionality, we can separate the numerical and categorical variables and remove the correlated variables. For numerical variables, we’ll use correlation. For categorical variables, we’ll use chi-square test.

Also, we can use PCA and pick the components which can explain the maximum variance in the data set.

Using online learning algorithms like Vowpal Wabbit (available in Python) is a possible option.

Building a linear model using Stochastic Gradient Descent is also helpful.

We can also apply our business understanding to estimate which all predictors can impact the response variable. But, this is an intuitive approach, failing to identify useful predictors might result in significant loss of information.

**Is rotation necessary in PCA? If yes, Why? What will happen if you don’t rotate the components?**

Yes, rotation (orthogonal) is necessary because it maximizes the difference between variance captured by the component. This makes the components easier to interpret. Not to forget, that’s the motive of doing PCA where, we aim to select fewer components (than features) which can explain the maximum variance in the data set. By doing rotation, the relative location of the components doesn’t change, it only changes the actual coordinates of the points.

If we don’t rotate the components, the effect of PCA will diminish and we’ll have to select more number of components to explain variance in the data set.

**You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?**

This question has enough hints for you to start thinking! Since, the data is spread across median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

**You are given a data set on cancer detection. You’ve build a classification model and achieved an accuracy of 96%. Why shouldn’t you be happy with your model performance? What can you do about it?**

If you have worked on enough data sets, you should deduce that cancer detection results in imbalanced data. In an imbalanced data set, accuracy should not be used as a measure of performance because 96% (as given) might only be predicting majority class correctly, but our class of interest is minority class (4%) which is the people who actually got diagnosed with cancer. Hence, in order to evaluate model performance, we should use Sensitivity (True Positive Rate), Specificity (True Negative Rate), F measure to determine class wise performance of the classifier. If the minority class performance is found to to be poor, we can undertake the following steps:

We can use undersampling, oversampling or SMOTE to make the data balanced.

We can alter the prediction threshold value by doing probability caliberation and finding a optimal threshold using AUC-ROC curve.

We can assign weight to classes such that the minority classes gets larger weight.

We can also use anomaly detection.

**Why is naive Bayes so ‘naive’ ?**

naive Bayes is so ‘naive’ because it assumes that all of the features in a data set are equally important and independent. As we know, these assumption are rarely true in real world scenario.

**Explain prior probability, likelihood and marginal likelihood in context of naiveBayes algorithm?**

Prior probability is nothing but, the proportion of dependent (binary) variable in the data set. It is the closest guess you can make about a class, without any further information. For example: In a data set, the dependent variable is binary (1 and 0). The proportion of 1 (spam) is 70% and 0 (not spam) is 30%. Hence, we can estimate that there are 70% chances that any new email would be classified as spam.

Likelihood is the probability of classifying a given observation as 1 in presence of some other variable. For example: The probability that the word ‘FREE’ is used in previous spam message is likelihood. Marginal likelihood is, the probability that the word ‘FREE’ is used in any message.

**You are working on a time series data set. You manager has asked you to build a high accuracy model. You start with the decision tree algorithm, since you know it works fairly well on all kinds of data. Later, you tried a time series regression model and got higher accuracy than decision tree model. Can this happen? Why?**

Time series data is known to posses linearity. On the other hand, a decision tree algorithm is known to work best to detect non – linear interactions. The reason why decision tree failed to provide robust predictions because it couldn’t map the linear relationship as good as a regression model did. Therefore, we learned that, a linear regression model can provide robust prediction given the data set satisfies its linearity assumptions.

**You are assigned a new project which involves helping a food delivery company save more money. The problem is, company’s delivery team aren’t able to deliver food on time. As a result, their customers get unhappy. And, to keep them happy, they end up delivering food for free. Which machine learning algorithm can save them?**

You might have started hopping through the list of ML algorithms in your mind. But, wait! Such questions are asked to test your machine learning fundamentals.

This is not a machine learning problem. This is a route optimization problem. A machine learning problem consist of three things:

There exist a pattern.

You cannot solve it mathematically (even by writing exponential equations).

You have data on it.

Always look for these three factors to decide if machine learning is a tool to solve a particular problem.

**You came to know that your model is suffering from low bias and high variance. Which algorithm should you use to tackle it? Why?**

Low bias occurs when the model’s predicted values are near to actual values. In other words, the model becomes flexible enough to mimic the training data distribution. While it sounds like great achievement, but not to forget, a flexible model has no generalization capabilities. It means, when this model is tested on an unseen data, it gives disappointing results.

In such situations, we can use bagging algorithm (like random forest) to tackle high variance problem. Bagging algorithms divides a data set into subsets made with repeated randomized sampling. Then, these samples are used to generate a set of models using a single learning algorithm. Later, the model predictions are combined using voting (classification) or averaging (regression).

Also, to combat high variance, we can:

Use regularization technique, where higher model coefficients get penalized, hence lowering model complexity.

Use top n features from variable importance chart. May be, with all the variable in the data set, the algorithm is having difficulty in finding the meaningful signal.

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