Today's Quiz -  Difference Between Bagging and Boosting?
Company Name - Latentview Analytics

Role - Data Scientist


Bagging and Boosting are two types of Ensemble Learning, which helps to improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model.

So, let’s understand the difference between Bagging and Boosting?

Bagging(Bootstrap aggregation): It is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average. Boosting: It is also a homogeneous weak learners’ model. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.

If the classifier is unstable (high variance), then we need to apply bagging. If the classifier is steady and straightforward (high bias), then we need to apply boosting.

In bagging, different training data subsets are randomly drawn with replacement from the entire training dataset. In boosting, every new subset contains the elements that were misclassified by previous models.

Bagging simplest way of combining predictions that belong to the same type. Boosting is the  way of combining predictions that belong to the different types.

Each model is built independently for bagging. While in the case of boosting, new models are influenced by the performance of previously built models.

Bagging attempts to tackle the overfitting issue. Boosting tries to reduce bias.

Example: The Random Forest model uses Bagging. The AdaBoost uses Boosting techniques

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Today's Quiz -  What is the difference between Rank and Dense Rank function?
Company Name - Myntra

Role - Data Analyst


An analytic function computes values over a group of rows and returns a single result for each row. This is different from an aggregate function, which returns a single result for a group of rows.

With analytic functions you can compute moving averages, rank items, calculate cumulative sums, and perform other analyses.

RANK gives you the ranking within your ordered position. Ties are assigned the same rank, with the next ranking(s) skipped.

DENSE_RANK again gives you the ranking within your ordered partition, but the ranks are consecutive. No ranks are skipped if there are ranks with multiple items.

Example below.. which is order partitioned on salary

Salary.       Rank.      Dense rank

1000           1                  1
2000           2                  2

2000           2                  2

2000           2                  2

3000           5                  3

Due to next rankings skipped in the case of RANKS, generally DENSE_RANK is preferred as it gives proper ranking when we want to calculate nth highest salary

Today's Quiz - If the probability of seeing a shooting star in 15mins is 20%. What is the probability of seeing at least one shooting star in one hour?
Company Name - TheMathCompany

Role - Data Analyst


Here it means, 20% probability = 20/100 = 1/5
Probability of Seeing a Star in 15 minutes = 1/5
Probability of not seeing a Star in 15 minutes = 1 - 1/5  = 4/5
Probability that you see at least one shooting star in the period of an hour
= 1 - Probability of not seeing any Star in 60 minutes
= 1 - Probability of not seeing any Star in 15 * 4 minutes
= 1 - (4/5)⁴
= 1 - 0.4096
= 0.5904
 
So, the probability of seeing at least one shooting star in a period of an hour is 0.594

Today's Quiz -  What is the statistical power of sensitivity?
Company Name - Hotstar

Role - Data Analyst


The statistical power of a study (sometimes called sensitivity) is how likely the study is to distinguish an actual effect from one of chance.

It’s the likelihood that the test is correctly rejecting the null hypothesis. For example, a study that has an 80% power means that the study has an 80% chance of the test having significant results.

A high statistical power means that the test results are likely valid. As the power increases, the probability of making a Type II error decreases.

A low statistical power means that the test results are questionable.

Statistical power helps you to determine if your sample size is large enough.

It is possible to perform a hypothesis test without calculating the statistical power. If your sample size is too small, your results may be inconclusive when they may have been conclusive if you had a large enough sample.

Today's Quiz -  How will you estimate the number of weddings that take place in a year in India?
Company Name - Hotstar

Role - Data Analyst


Facts
India's population in a year - 1.3 bill
Population breakup - Rural - 70% and Urban - 30%

Assumptions
Every year India's population would grow steadily, but the growth won't be very fast-paced.
Every man and women would be eventually married (homogeneously or heterogeneously). They won't prematurely die or prefer not to marry. People would be married only once.
In rural areas the age of marriage (in average) is between 15 - 35 year range. Similarly, in urban areas = 20 - 35 years. India is a young country, and 15 - 35 year range has around 50% of the total population.

Rural estimation
Rural population = 70% * 1.3 bill = 900 mill
Population within marriage age in a year = 50% * 900 mill = 450 mill
Number of marriages to happen = 450 / 2 = 225 mill marriages
These people will marry within a 20 year time period according to our assumptions.
Number of rural marriages in a year = 225 mill / 20 = 11.25 mill marriages
 
Urban estimation
Urban population = 30% * 1.3 bill = 400 mill
Population within marriage age in a year = 50% * 400 mill = 200 mill
Number of marriages to happen = 200 / 2 = 100 mill marriages
These people will marry within a 15 year time period according to our assumptions.
Number of urban marriages in a year = 100 mill / 15 = 6.6 mill marriages
 
Many people die in accidents prematurely, and won’t marry. In addition, most people don't marry as well as a consumer preference parameter. So, our market number is over-estimated. Even if we try to normalize it by introducing an error percentage of around 10%, the final number number will be lesser by around 10%-15%.
Answer = Approximately 14 million marriages occur in a year in India

Today's Quiz -  What is Bayes’ Theorem? How is it useful in a machine learning context?
Company Name - MakeMyTrip

Role - Data Science Intern


Bayes' theorem, also known as Bayes' rule or Bayes' law, is a theorem in statistics that describes the probability of one event or condition as it relates to another known event or condition.

Mathematically, it’s expressed as the true positive rate of a condition sample divided by the sum of the false positive rate of the population and the true positive rate of a condition.

Let’s understand this theorem with an example:

For instance, say you had a 60% chance of actually having the flu after a flu test, but out of people who had the flu, the test will be false 50% of the time, and the overall population only has a 5% chance of having the flu. Would you actually have a 60% chance of having the flu after having a positive test?

Bayes’ Theorem says no. It says that you have a (.6 * 0.05) (True Positive Rate of a Condition Sample) / (.6*0.05)(True Positive Rate of a Condition Sample) + (.5*0.95) (False Positive Rate of a Population)  = 0.0594 or 5.94% chance of getting a flu.

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

Naive Bayes uses a similar method to predict the probability of different class based on various attributes. This algorithm is mostly used in text classification and with problems having multiple classes.

Today's Quiz -  What are Loss Function and Cost Functions? Explain the key Difference Between them?
Company Name - Landmark Group

Role - Data Scientist


When calculating loss we consider only a single data point, then we use the term loss function.
Whereas, when calculating the sum of error for multiple data then we use the cost function.
In other words, the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset.
The most commonly used loss functions are Mean-squared error and Hinge loss.
Mean-Squared Error(MSE): In simple words, we can say how our model predicted values against the actual values.
                                 MSE = √(predicted value - actual value)2
Hinge loss: It is used to train the machine learning classifier, which is
                                  L(y) = max(0,1- yy)
Where y = -1 or 1 indicating two classes and y represents the output form of the classifier. The most common cost function represents the total cost as the sum of the fixed costs and the variable costs in the equation y = mx + b.

There are many cost functions in machine learning and each has its use cases depending on whether it is a regression problem or classification problem.

Regression cost function:
Regression models are used to forecast a continuous variable, such as an employee's pay, the cost of a car, the likelihood of obtaining a loan, and so on. They are determined as follows depending on the distance-based error:
                                      Error = y-y’
Where, Y – Actual Input and Y’ – Predicted output

Today's Quiz -  If 70% of Facebook users on iOS use Instagram, but only 35% of Facebook users on Android use Instagram, how would you investigate the discrepancy?
Company Name - Facebook

Role - Data Scientist


There are a number of possible variables that can cause such a discrepancy that I would check to see:

The demographics of iOS and Android users might differ significantly. For example, according to Hootsuite, 43% of females use Instagram as opposed to 31% of men. If the proportion of female users for iOS is significantly larger than for Android then this can explain the discrepancy (or at least a part of it). This can also be said for age, race, ethnicity, location, etc.…

Behavioral factors can also have an impact on the discrepancy. If iOS users use their phones more heavily than Android users, it's more likely that they'll indulge in Instagram and other apps than someone who spent significantly less time on their phones.

Another possible factor to consider is how Google Play and the App Store differ. For example, if Android users have significantly more apps (and social media apps) to choose from, that may cause greater dilution of users.

Lastly, any differences in the user experience can deter Android users from using Instagram compared to iOS users. If the app is more buggy for Android users than iOS users, they'll be less likely to be active on the app.

Today's Quiz - How would you evaluate a logistic regression model?
Company Name - Infosys

Role - Data Scientist


Model Evaluation is a very important part in any analysis to answer the following questions,
How well does the model fit the data? Which predictors are most important? Are the predictions accurate?
So, the following are the criterion to access the model performance,
1. Akaike Information Criteria (AIC): In simple terms, AIC estimates the relative amount of information lost by a given model. So, the less information lost the higher the quality of the model. Therefore, we always prefer models with minimum AIC.
2. Receiver operating characteristics (ROC curve): ROC curve illustrates the diagnostic ability of a binary classifier. It is calculated/ created by plotting True Positive against False Positive at various threshold settings. The performance metric of ROC curve is AUC (area under curve). Higher the area under the curve, better the prediction power of the model.
3. Confusion Matrix: In order to find out how well the model does in predicting the target variable, we use a confusion matrix/ classification rate. It is nothing but a tabular representation of actual Vs predicted values which helps us to find the accuracy of the model.

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Today's Quiz -  What is Pruning in Decision Trees, and How Is It Done?
Company Name -  Landmark Group

Role - Data Scientist


Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.
Pruning processes can be divided into two types (pre- and post-pruning).
Pre-pruning procedures prevent a complete induction of the training set by replacing a stop () criterion in the induction algorithm. Pre-pruning methods are considered to be more efficient because they do not induce an entire set, but rather trees remain small from the start
Post-pruning is the most common way of simplifying trees. Here, nodes and subtrees are replaced with leaves to reduce complexity.
The procedures are differentiated on the basis of their approach in the tree (top-down or bottom-up).
Top-down fashion:  It will traverse nodes and trim subtrees starting at the root.
Bottom-up fashion: It will begin at the leaf nodes.

There is a popular pruning algorithm called reduced error pruning, in which starting at the leaves, each node is replaced with its most popular class. If the prediction accuracy is not affected, the change is kept.

Today's Quiz -  Difference between forecasting and prediction?
Company Name -  L&T Infotech

Role - Data Scientist


A forecast refers to a calculation or an estimation which uses data from previous events, combined with recent trends to come up a future event outcome.
On the other hand, a prediction is an actual act of indicating that something will happen in the future with or without prior information. 

Accuracy: A Forecast is more accurate compared to a prediction. This is because forecasts are derived by analyzing a set of past data from the past and presents trends.
On the other hand, a prediction can be right or wrong. For example, if you predict the outcome of a football match, the result depends on how well the teams played no matter their recent performance or players.

Bias: Forecasting uses mathematical formulas and as a result, they are free from personal as well as intuition bias.
On the other hand, predictions are in most cases subjective and fatalistic in nature.

Quantification: When using a model to do a forecast, it’s possible to come up with the exact quantity. For example, the World Bank uses economic trends, and the previous GDP values and other inputs to come up with a percentage value for a country economic growth. 
However, when doing prediction, since there is no data for processing, one can only say the economy of a given country will grow or not.

Application: Forecasts are only applicable in the economic and meteorology field where there is a lot of information about the subject matter.
On the contrary, prediction can be applied anywhere as long as there is an expected future outcome.   

Today's Quiz - Why do we need a validation set and a test set?
Company Name -  Infosys

Role - Data Scientist


The data is split into three different categories while creating a model:
Training set: We use the training set for building the model and adjusting the model’s variables. But we cannot rely on the correctness of the model build on top of the training set. The model might give incorrect outputs on feeding new inputs.
Validation set: We use a validation set to look into the model’s response on top of the samples that don’t exist in the training dataset. Then, we will tune hyperparameters on the basis of the estimated benchmark of the validation data.
When we are evaluating the model’s response using the validation set, we are indirectly training the model with the validation set. This may lead to the overfitting of the model to specific data. So, this model won’t be strong enough to give the desired response to the real-world data.
Test set: The test dataset is the subset of the actual dataset, which is not yet used to train the model. The model is unaware of this dataset. So, by using the test dataset, we can compute the response of the created model on hidden data. We evaluate the model’s performance on the basis of the test dataset.
The model is always exposed to the test dataset after tuning the hyperparameters on top of the validation set.
 As we know, the evaluation of the model on the basis of the validation set would not be enough. Thus, we use a test set for computing the efficiency of the model.

Today's Quiz -  What are some ways I can make my model more robust to outliers?
Company Name -   Bridgei2i Analytics.

Role - Data Scientist


There are several ways to make a model more robust to outliers, from different points of view (data preparation or model building). An outlier in the question and answer is assumed being unwanted, unexpected, or a must-be-wrong value to the human’s knowledge so far (e.g. no one is 200 years old) rather than a rare event which is possible but rare.

Outliers are usually defined in relation to the distribution. Thus outliers could be removed in the pre-processing step (before any learning step), by using standard deviations (Mean +/- 2*SD), it can be used for normality. Or interquartile ranges Q1 - Q3, Q1 - is the "middle" value in the first half of the rank-ordered data set, Q3 - is the "middle" value in the second half of the rank-ordered data set. It can be used for not normal/unknown as threshold levels.

Moreover, data transformation (e.g. log transformation) may help if data have a noticeable tail. When outliers related to the sensitivity of the collecting instrument which may not precisely record small values, Winsorization may be useful. This type of transformation has the same effect as clipping signals (i.e. replaces extreme data values with less extreme values). Another option to reduce the influence of outliers is using mean absolute difference rather mean squared error.

For model building, some models are resistant to outliers (e.g. tree-based approaches) or non-parametric tests. Similar to the median effect, tree models divide each node into two in each split. Thus, at each split, all data points in a bucket could be equally treated regardless of extreme values they may have.

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Today's Quiz - How would you predict who will renew their subscription next month? What data would you need to solve this? What analysis would you do? Would you build predictive models? If so, which algorithms?
Company Name -   Cognizant

Role - Data Scientist


Let’s assume that we’re trying to predict renewal rate for Netflix subscription. So our problem statement is to predict which users will renew their subscription plan for the next month.

Next, we must understand the data that is needed to solve this problem. In this case, we need to check the number of hours the channel is active for each household, the number of adults in the household, number of kids, which channels are streamed the most, how much time is spent on each channel, how much has the watch rate varied from last month, etc. Such data is needed to predict whether or not a person will continue the subscription for the upcoming month.

After collecting this data, it is important that you find patterns and correlations. For example, we know that if a household has kids, then they are more likely to subscribe. Similarly, by studying the watch rate of the previous month, you can predict whether a person is still interested in a subscription. Such trends must be studied.

The next step is analysis. For this kind of problem statement, you must use a classification algorithm that classifies customers into 2 groups:

Customers who are likely to subscribe next month

Customers who are not likely to subscribe next month

Would you build predictive models? Yes, in order to achieve this you must build a predictive model that classifies the customers into 2 classes like mentioned above.

Which algorithms to choose? You can choose classification algorithms such as Logistic Regression, Random Forest, Support Vector Machine, etc.

Once you’ve opted the right algorithm, you must perform model evaluation to calculate the efficiency of the algorithm. This is followed by deployment.

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