Web Intelligence And Big Data Homework 3 Quadratic Equations


Logistic Regression is likely the most commonly used algorithm for solving all classification problems. It is also one of the first methods people get their hands dirty on.

We saw the same spirit on the test we designed to assess people on Logistic Regression. More than 800 people took this test. This skill test is specially designed for you to test your knowledge on logistic regression and its nuances.

If you are one of those who missed out on this skill test, here are the questions and solutions. You missed on the real time test, but can read this article to find out how many could have answered correctly.

Here is the leaderboard for the participants who took the test.


Overall Distribution

Below is the distribution of the scores of the participants:

You can access the scores here. More than 800 people participated in the skill test and the highest score obtained was 27.


Helpful Resources

Here are some resources to get in depth knowledge in the subject.


Skill test Questions and Answers

1) True-False: Is Logistic regression a supervised machine learning algorithm?


Solution: A

True, Logistic regression is a supervised learning algorithm because it uses true labels for training. Supervised learning algorithm should have input variables (x) and an target variable (Y) when you train the model .


2) True-False: Is Logistic regression mainly used for Regression?


Solution: B

Logistic regression is a classification algorithm, don’t confuse with the name regression.


3) True-False: Is it possible to design a logistic regression algorithm using a Neural Network Algorithm?


Solution: A

True, Neural network is a is a universal approximator so it can implement linear regression algorithm.


4) True-False: Is it possible to apply a logistic regression algorithm on a 3-class Classification problem?


Solution: A

Yes, we can apply logistic regression on 3 classification problem, We can use One Vs all method for 3 class classification in logistic regression.


5) Which of the following methods do we use to best fit the data in Logistic Regression?

A) Least Square Error
B) Maximum Likelihood
C) Jaccard distance
D) Both A and B

Solution: B

Logistic regression uses maximum likely hood estimate for training a logistic regression.


6) Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?

B) Accuracy
C) Logloss
D) Mean-Squared-Error

Solution: D

Since, Logistic Regression is a classification algorithm so it’s output can not be real time value so mean squared error can not use for evaluating it


7) One of the very good methods to analyze the performance of Logistic Regression is AIC, which is similar to R-Squared in Linear Regression. Which of the following is true about AIC?

A) We prefer a model with minimum AIC value
B) We prefer a model with maximum AIC value
C) Both but depend on the situation
D) None of these

Solution: A

We select the best model in logistic regression which can least AIC. For more information refer this source: http://www4.ncsu.edu/~shu3/Presentation/AIC.pdf


8) [True-False] Standardisation of features is required before training a Logistic Regression.


Solution: B

Standardization isn’t required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization.


9) Which of the following algorithms do we use for Variable Selection?

B) Ridge
C) Both
D) None of these

Solution: A

In case of lasso we apply a absolute penality, after increasing the penality in lasso some of the coefficient of variables may become zero.


Context: 10-11

Consider a following model for logistic regression: P (y =1|x, w)= g(w0 + w1x)
where g(z) is the logistic function.

In the above equation the P (y =1|x; w) , viewed as a function of x, that we can get by changing the parameters w.

10) What would be the range of p in such case?

A) (0, inf)
B) (-inf, 0 )
C) (0, 1)
D) (-inf, inf)

Solution: C

For values of x in the range of  real number from −∞ to +∞ Logistic function will give the output between (0,1)


11) In above question what do you think which function would make p between (0,1)?

A) logistic function
B) Log likelihood function
C) Mixture of both
D) None of them

Solution: A

Explanation is same as question number 10


Context: 12-13

Suppose you train a logistic regression classifier and your hypothesis function H is



12) Which of the following figure will represent the decision boundary as given by above classifier?






Solution: B

Option B would be the right answer. Since our line will be represented by y = g(-6+x2) which is shown in the option A and option B. But option B is the right answer because when you put the value x2 = 6 in the equation then y = g(0) you will get that means y= 0.5 will be on the line, if you increase the value of x2 greater then 6 you will get negative values so output will be the region y =0.


13) If you replace coefficient of x1 with x2 what would be the output figure?





Solution: D

Same explanation as in previous question.


14) Suppose you have been given a fair coin and you want to find out the odds of getting heads. Which of the following option is true for such a case?

A) odds will be 0
B) odds will be 0.5
C) odds will be 1
D) None of these

Solution: C

Odds are defined as the ratio of the probability of success and the probability of failure. So in case of fair coin probability of success is 1/2 and the probability of failure is 1/2 so odd would be 1


15) The logit function(given as l(x)) is the log of odds function. What could be the range of logit function in the domain x=[0,1]?

A) (– ∞ , ∞)
B) (0,1)
C) (0, ∞)
D) (- ∞, 0)

Solution: A

For our purposes, the odds function has the advantage of transforming the probability function, which has values from 0 to 1, into an equivalent function with values between 0 and ∞. When we take the natural log of the odds function, we get a range of values from -∞ to ∞.


16) Which of the following option is true?

A) Linear Regression errors values has to be normally distributed but in case of Logistic Regression it is not the case
B) Logistic Regression errors values has to be normally distributed but in case of Linear Regression it is not the case
C) Both Linear Regression and Logistic Regression error values have to be normally distributed
D) Both Linear Regression and Logistic Regression error values have not to be normally distributed


Only A is true. Refer this tutorial https://czep.net/stat/mlelr.pdf


17) Which of the following is true regarding the logistic function for any value “x”?

Logistic(x): is a logistic function of any number “x”

Logit(x): is a logit function of any number “x”

Logit_inv(x): is a inverse logit function of any number “x”

A) Logistic(x) = Logit(x)
B) Logistic(x) = Logit_inv(x)
C) Logit_inv(x) = Logit(x)
D) None of these

Solution: B

Refer this link for the solution: https://en.wikipedia.org/wiki/Logit


18) How will the bias change on using high(infinite) regularisation?

Suppose you have given the two scatter plot “a” and “b” for two classes( blue for positive and red for negative class). In scatter plot “a”, you correctly classified all data points using logistic regression ( black line is a decision boundary).


A) Bias will be high
B) Bias will be low
C) Can’t say
D) None of these

Solution: A

Model will become very simple so bias will be very high.


19) Suppose, You applied a Logistic Regression model on a given data and got a training accuracy X and testing accuracy Y. Now, you want to add a few new features in the same data. Select the option(s) which is/are correct in such a case.

Note: Consider remaining parameters are same.

A) Training accuracy increases
B) Training accuracy increases or remains the same
C) Testing accuracy decreases
D) Testing accuracy increases or remains the same

Solution: A and D

Adding more features to model will increase the training accuracy because model has to consider more data to fit the logistic regression. But testing accuracy increases if feature is found to be significant


20) Choose which of the following options is true regarding One-Vs-All method in Logistic Regression.

A) We need to fit n models in n-class classification problem
B) We need to fit n-1 models to classify into n classes
C) We need to fit only 1 model to classify into n classes
D) None of these

Solution: A

If there are n classes, then n separate logistic regression has to fit, where the probability of each category is predicted over the rest of the categories combined.


21) Below are two different logistic models with different values for β0 and β1.

Which of the following statement(s) is true about β0 and β1 values of two logistics models (Green, Black)?

Note: consider Y = β0 + β1*X. Here, β0 is intercept and β1 is coefficient.

A) β1 for Green is greater than Black
B) β1 for Green is lower than Black
C) β1 for both models is same
D) Can’t Say

Solution: B

β0 and β1: β0 = 0, β1 = 1 is in X1 color(black) and β0 = 0, β1 = −1 is in X4 color (green)


Context 22-24

Below are the three scatter plot(A,B,C left to right) and hand drawn decision boundaries for logistic regression.

22) Which of the following above figure shows that the decision boundary is overfitting the training data?

A) A
B) B
C) C
D)None of these

Solution: C

Since in figure 3, Decision boundary is not smooth that means it will over-fitting the data.


23) What do you conclude after seeing this visualization?

  1. The training error in first plot is maximum as compare to second and third plot.
  2. The best model for this regression problem is the last (third) plot because it has minimum training error (zero).
  3. The second model is more robust than first and third because it will perform best on unseen data.
  4. The third model is overfitting more as compare to first and second.
  5. All will perform same because we have not seen the testing data.

A) 1 and 3
B) 1 and 3
C) 1, 3 and 4
D) 5

Solution: C

The trend in the graphs looks like a quadratic trend over independent variable X. A higher degree(Right graph) polynomial might have a very high accuracy on the train population but is expected to fail badly on test dataset. But if you see in left graph we will have training error maximum because it underfits the training data


24) Suppose, above decision boundaries were generated for the different value of regularization. Which of the above decision boundary shows the maximum regularization?

A) A
B) B
C) C
D) All have equal regularization

Solution: A

Since, more regularization means more penality means less complex decision boundry that shows in first figure A.


25) The below figure shows AUC-ROC curves for three logistic regression models. Different colors show curves for different hyper parameters values. Which of the following AUC-ROC will give best result?

A) Yellow
B) Pink
C) Black
D) All are same

Solution: A

The best classification is the largest area under the curve so yellow line has largest area under the curve.


26) What would do if you want to train logistic regression on same data that will take less time as well as give the comparatively similar accuracy(may not be same)?

Suppose you are using a Logistic Regression model on a huge dataset. One of the problem you may face on such huge data is that Logistic regression will take very long time to train.

A) Decrease the learning rate and decrease the number of iteration
B) Decrease the learning rate and increase the number of iteration
C) Increase the learning rate and increase the number of iteration
D) Increase the learning rate and decrease the number of iteration

Solution: D

If you decrease the number of iteration while training it will take less time for surly but will not give the same accuracy for getting the similar accuracy but not exact you need to increase the learning rate.


27) Which of the following image is showing the cost function for y =1.

Following is the loss function in logistic regression(Y-axis loss function and x axis log probability) for two class classification problem.

Note: Y is the target class

A) A
B) B
C) Both
D) None of these

Solution: A

A is the true answer as loss function decreases as the log probability increases


28) Suppose, Following graph is a cost function for logistic regression.

Now, How many local minimas are present in the graph?

A) 1
B) 2
C) 3
D) 4

Solution: C

There are three local minima present in the graph


29) Imagine, you have given the below graph of logistic regression  which is shows the relationships between cost function and number of iteration for 3 different learning rate values (different colors are showing different curves at different learning rates ). 


Suppose, you save the graph for future reference but you forgot to save the value of different learning rates for this graph. Now, you want to find out the relation between the leaning rate values of these curve. Which of the following will be the true relation?


  1. The learning rate for blue is l1
  2. The learning rate for red is l2
  3. The learning rate for green is l3

A) l1>l2>l3
B) l1 = l2 = l3
C) l1 < l2 < l3

D) None of these

Solution: C

If you have low learning rate means your cost function will decrease slowly but in case of large learning rate cost function will decrease very fast.


30) Can a Logistic Regression classifier do a perfect classification on the below data?



Note: You can use only X1 and X2 variables where X1 and X2 can take only two binary values(0,1).

C) Can’t say
D) None of these

Solution: B

No, logistic regression only forms linear decision surface, but the examples in the figure are not linearly separable.



End Notes

I tried my best to make the solutions as comprehensive as possible but if you have any questions / doubts please drop in your comments below. I would love to hear your feedback about the skill test. For more such skill tests, check out our current hackathons.

Learn, engage,compete, and get hired!

I spent the last few weeks digging deeper into time series-related methods and data mining methods. For this post, I have decided to write a broad introduction related to the latter (data mining), since it may look more practical and also trendier than time-series (but watch out with the emergence of Particle Filtering and other Sequential Monte Carlo methods (SMC)).

Introduction to Data Mining and Criss Angel

So what is data mining? Data mining can be defined as the process but also the “art” of discovering/mining patterns, meaning and insights in large datasets by using statistical and computational methods. In other words, a data miner is like a Criss Angel (You can pick any other magician here!) that will make appear from your messy ocean of data, insights that will be valuable to your company and may give you a competitive advantage compared to your competitors; simply read Tom Davenport’s bestselling book “Competing on Analytics: The New Science of Winning” if you’re not convinced yet about the power of analytics and by extension of data mining. Furthermore, data mining related tasks are also considered as part of a more general process called Knowledge discovery in databases (KDD) which includes the “art” of collecting the right data as well as organizing and cleaning these data, which are also extremely important tasks prior to analyzing the data.

Some Brief History and a Link to Business Intelligence

Data mining mainly takes its roots from the fields of Statistics and Computer Science (some might say Artificial Intelligence) and may also be referred as “Statistical Learning”. From a statistical perspective, most early and recent advances coming from Statistics have come from the Stanford Statistics department school of thoughts (Leo Breiman (was at UC Berkeley), Bradley Efron, Jerome H. Friedman, Trevor Hastie and Robert Tibshirani). By the way, don’t forget that Stanford University is only 7 miles away from Google. Furthermore, the emerging field of Business Intelligence has blossomed as a combination of: (1) data mining tasks, (2) information systems technology and (3) crispy marketing insights.

Types of Data Mining Methods and Marketing

Data mining methods can be divided in multiple ways. However, most books on the topic, and especially those related to marketing and business intelligence, will generally divide data mining methods into two types, the ones related to supervised learning and the ones related to unsupervised learning.

Supervised Learning

Supervised learning is often more associated to scientific research as it includes tasks where the data miner needs to describe or predict the relationship between a set of independent variables (also referred to as inputs, features) and a dependent variable (also referred to as outcome, output or a target variable). Moreover, the dependent variable can be categorical (i.e. churn rate or classes of customers) or continuous (i.e. money earned from that customer) while the independent variables may be of any type but needs to be coded properly (i.e. dividing the categorical variables into separate binary variables). From a marketing and business intelligence perspective, I will divide supervised learning into two interrelated tasks: (1) supervised classification tasks and (2) Predictive Analysis.

Supervised Classification tasks: Supervised classification tasks occurred when you want to predict correctly to which class/category (this is the dependent variable) belong the new observations (i.e. customers) based on results from an already known training dataset. Generally, you will achieve this task by using: (1) a training dataset, (2) a validation dataset and (3) a test dataset. Most known methods I’m using for these tasks are the following:

1. Multinomial Logit (MNL)
2. Linear Discriminant Analysis (LDA)
3. Quadratic Discriminant Analysis (QDA)
4. Flexible Discriminant Analysis with Multivariate Adaptive Regression Splines (FDA – MARS)
5. Penalized Discriminant Analysis (PDA)
6. Mixture Discriminant Analysis (MDA)
7. Naïve Bayes Classifier (NBC)
8. K-Nearest Neighbor (KNN)
9. Support Vector Machines with multiple Kernels (SVM)
10. Classification and Regression Trees (CART)
11. Bagging
12. Boosting
13. Random Forests
14. Neural Networks

Predictive Analysis: I’ve decided to include the expression “Predictive Analysis” here, since it’s a buzzword in the web community nowadays. However, any task related to supervised classification involve a so-called “Predictive Analysis”. However, “Predictive Analysis” is a broader expression that also includes tasks related to the prediction of a continuous dependent variable rather than a categorical variable. Additional methods which can’t be used to conduct classification analyses may be used for predictive analyses with continuous variables and vice-versa.

Unsupervised learning

Unsupervised learning is when the data miner task is to detect patterns based only on independent variables. It is generally presented more from an algorithmic fashion rather than from a purely statistical fashion. Well-known methods applied to marketing includes: (1) Market Basket Analysis and (2) Clustering.

Market Basket Analysis: Market basket analysis (also abbreviated as MBA to confuse you even more) is certainly one of the most known and easier task relating data mining and marketing. It is considered more as a typical marketing application rather than as a data mining method. It can be simplified as a simple Amazon recommendation algorithm showing as an association rule that “the probability that customers who bought item A also bought item B is 56%”. The classic urban legend about Market Basket Analysis is the “beer” and “diapers” association where a large supermarket chain, most people will say Walmart, did a Market Basket Analysis of customers’ buying habits and found an association between beer purchases and diapers purchases. It was theorized that the reason for this was that fathers were stopping off at Walmart to buy diapers for their babies, and since they could no longer go to bars and pubs as often as before, they would buy beer as well. As a result of this finding, the supermarket chain managers have placed the diapers next to the beer in the aisles, resulting in increased sales for both products.

Clustering: The method of Clustering is defined as the assignment of a set of observations (customers) in subsets (clusters) where customers in a cluster are similar to each other while they are different from other customers in other clusters. Clustering is often used in marketing for segmentation tasks. However, even though segmentation may be achieved through “clustering”, more modern supervised methods such as Bayesian Mixture Models, which I must say are not really part of the data mining field, are used by the few practitioners who can actually understand how to program this method (this is one method I am programming these days). For more about segmentation, I would refer anyone to the book “Market Segmentation: Conceptual and Methodological Foundations” by Michel Wedel and Wagner A. Kamakura, both are professors and well-known authorities on the topic.

Some Top References

I must say without a doubt that the best book I know about data mining is surely “Elements of Statistical Learning” by Stanford Professors Trevor Hastie, Robert Tibshirani, and Jerome H. Friedman which covers broadly and nearly every type of methods you can use to conduct data mining tasks. However I must admit that this book has a focus on the statistics behind the methods (but it’s extremely clear) rather than on the software tools (No, it’s not a cookbook) you could use to conduct these analyses, and it may also lack of marketing applications for a marketer. Furthermore, to get some updates about the data mining world, KD Nuggets, administered by Gregory Piatetsky-Shapiro, is actually THE reference for the data mining world.

Some Top Software

Here is a description of some software I would recommend for data mining tasks, feel free to propose your own software in the comments section:

1. R: R is actually my favorite software. I have been using the software for mainly all of my statistics-related tasks for the last 2 years. Its free, open source, it has an extensive and very knowledgeable community, it’s extremely intuitive and it can be learned more easily if you have knowledge of software such as C++, Python and/or GAUSS. Furthermore, there are a lot of useful packages available to facilitate the coding. However, I must say that compared to C++ or SAS, sometimes R can be slow for data mining tasks involving a heavy load of data.

2. rattle: rattle, which stands for the R Analytical Tool To Learn Easily, is a “point and click” data mining interface related to R and developed by Graham Williams of Togaware. Frankly, I must admit that this software rocks even though I generally don’t like “point and click” software. It’s extremely complete and quite easy to use.

3. SAS Enterprise Miner: SAS Enterprise Miner, a module in SAS, was the first software I used for performing data mining tasks. It is extremely fast and user-friendly. However, I must admit that it reminds me software like Amos, now included in PASW (formerly SPSS) for Structural Equation Modeling (SEM) tasks, where you move the “little truck” to build your model and don’t really understand what you’re doing at the end of the day. Furthermore, it costs a lot but to my knowledge, SAS is the only software platform integrating data mining tasks with web analytics and social media analytics.

4. RapidMiner: RapidMiner formerly known as YALE (Yet Another Learning Environment) is considered by multiple data miner as THE software to use to conduct data mining tasks. Similarly to R, the software is open source as well as free of charge for the “Community” version. I haven’t made the switch from R to RapidMiner yet and I am currently testing the software in depth.

5. Salford Systems: I must confess that I never used Salford Systems software but know them by reputation, thus, I can’t have a clear personal opinion on the software. However, statisticians working at Salford Systems are presenting workshops on data mining for the next Joint Statistical Meeting (JSM) in Miami at the end of July 2011 which I might attend.

Waiting time and Conclusion

Whatever the software you’re using, data mining-related tasks will always be demanding in terms of your computer memory. Data Mining in marketing and business intelligence and more broadly KDD is an art that requires strong statistical skills but also a great comprehension of marketing problems. So when you’re waiting for your data mining computations, feel free to come by and read my other cool posts on your other computer! In anyways, enjoy data mining and as one of my friend would say “show some respect to the machine”, but even more to the data miner!


Jean-Francois Belisle

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