Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, RF etc.). Sensitivity is nothing but “Predicted TRUE events/ Total events”. True events here are the events which were true and model also predicted them as true.
Calculation of senstivity is pretty straight forward-
Senstivity = True Positives /Positives in Actual Dependent Variable
Where, True positives are Positive events which are correctly classified as Positives.
What is the importance of having a selection bias?
Selection Bias occurs when there is no appropriate randomization acheived while selecting individuals, groups or data to be analysed.Selection bias implies that the obtained sample does not exactly represent the population that was actually intended to be analyzed.Selection bias consists of Sampling Bias, Data, Attribute and Time Interval.
Give some situations where you will use an SVM over a RandomForest Machine Learning algorithm and vice-versa.
SVM and Random Forest are both used in classification problems.
a) If you are sure that your data is outlier free and clean then go for SVM. It is the opposite - if your data might contain outliers then Random forest would be the best choice
b) Generally, SVM consumes more computational power than Random Forest, so if you are constrained with memory go for Random Forest machine learning algorithm.
c) Random Forest gives you a very good idea of variable importance in your data, so if you want to have variable importance then choose Random Forest machine learning algorithm.
d) Random Forest machine learning algorithms are preferred for multiclass problems.
e) SVM is preferred in multi-dimensional problem set - like text classification
but as a good data scientist, you should experiment with both of them and test for accuracy or rather you can use ensemble of many Machine Learning techniques.
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| Luxoft Most Frequently Asked Data Science Interview Questions Answers |
Python or R – Which one would you prefer for text analytics?
The best possible answer for this would be Python because it has Pandas library that provides easy to use data structures and high performance data analysis tools.
Which technique is used to predict categorical responses?
Classification technique is used widely in mining for classifying data sets.
What is logistic regression? Or State an example when you have used logistic regression recently.
Logistic Regression often referred as logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.
What are Recommender Systems?
A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.
Why data cleaning plays a vital role in analysis?
Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because - as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.
A jar has 1000 coins, of which 999 are fair and 1 is double headed. Pick a coin at random, and toss it 10 times. Given that you see 10 heads, what is the probability that the next toss of that coin is also a head?
There are two ways of choosing the coin. One is to pick a fair coin and the other is to pick the one with two heads.
Probability of selecting fair coin = 999/1000 = 0.999
Probability of selecting unfair coin = 1/1000 = 0.001
Selecting 10 heads in a row = Selecting fair coin * Getting 10 heads + Selecting an unfair coin
P (A) = 0.999 * (1/2)^5 = 0.999 * (1/1024) = 0.000976
P (B) = 0.001 * 1 = 0.001
P( A / A + B ) = 0.000976 / (0.000976 + 0.001) = 0.4939
P( B / A + B ) = 0.001 / 0.001976 = 0.5061
Probability of selecting another head = P(A/A+B) * 0.5 + P(B/A+B) * 1 = 0.4939 * 0.5 + 0.5061 = 0.7531
What are Eigenvalue and Eigenvector?
Eigenvectors are for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvalues are the directions along which a particular linear transformation acts by flipping, compressing or stretching.
Why is resampling done?
Resampling is done in any of these cases:
Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points
Substituting labels on data points when performing significance tests
Validating models by using random subsets (bootstrapping, cross validation)
Explain selective bias.?
Selection bias, in general, is a problematic situation in which error is introduced due to a non-random population sample.
What are the types of biases that can occur during sampling?
Selection bias
Under coverage bias
Survivorship bias
Explain survivorship bias.
It is the logical error of focusing aspects that support surviving some process and casually overlooking those that did not because of their lack of prominence. This can lead to wrong conclusions in numerous different means.
How do you work towards a random forest?
The underlying principle of this technique is that several weak learners combined to provide a strong learner. The steps involved are
Build several decision trees on bootstrapped training samples of data
On each tree, each time a split is considered, a random sample of mm predictors is chosen as split candidates, out of all pp predictors
Rule of thumb: At each split m=p√m=p
Predictions: At the majority rule
What are the basic assumptions to be made for linear regression?
Normality of error distribution, statistical independence of errors, linearity and additivity.
Can you write the formula to calculat R-square?
R-Square can be calculated using the below formular -
1 - (Residual Sum of Squares/ Total Sum of Squares)
Differentiate between univariate, bivariate and multivariate analysis.
These are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can be referred to as univariate analysis.
If the analysis attempts to understand the difference between 2 variables at time as in a scatterplot, then it is referred to as bivariate analysis. For example, analysing the volume of sale and a spending can be considered as an example of bivariate analysis.
Analysis that deals with the study of more than two variables to understand the effect of variables on the responses is referred to as multivariate analysis.
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