Data Science Interview Questions

z-score

the number of standard deviations a particular score is from the mean

How can we identify outliers?

STD, IQR, z-score, Boxplot

Type 1 error

rejecting the null hypothesis when it is true (false positive)

MAE

(mean absolute error) - measure of difference between two continuous variables - As compared to mean absolute error, RMSE gives higher weightage and punishes large errors.

What are the important skills to have in Python with regard to data analysis?

- Good understanding of the built-in data types especially lists, dictionaries, tuples, and sets. - Mastery of N-dimensional NumPy Arrays. - Mastery of Pandas dataframes. - Ability to perform element-wise vector and matrix operations on NumPy arrays. - Knowing that you should use the Anaconda distribution and the conda package manager. - Familiarity with Scikit-learn. **Scikit-Learn Cheat Sheet** - Ability to write efficient list comprehensions instead of traditional for loops. - Ability to write small, clean functions (important for any developer), preferably pure functions that don't alter objects. - Knowing how to profile the performance of a Python script and how to optimize bottlenecks.

How is k nearest neighbor different from k means?

- K-nearest neighbors is for labeled/classification/structured data; K is the # of neighbors - K-means is used for unlabeled /clustering/unstructured data; K is the # of clusters; use sum of squares to create an elbow chart to pick number of clusters(k)

What does a Data Analyst do?

- business administration - exploratory data analysis

Explain Gradient Descent.

A gradient measures how much the output of a function changes if you change the inputs a little bit. It simply measures the change in all weights with regard to the change in error. You can also think of a gradient as the slope of a function. Gradient Descent can be thought of climbing down to the bottom of a valley, instead of climbing up a hill. This is because it is a minimization algorithm that minimizes a given function (Activation Function).

How would you handle an imbalance dataset?

Get more data, cut out some of the other sample so that it is more balance. And try a different algorithm that can handle the imbalance better.

What are the differences between overfitting and underfitting?

In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data. In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data. Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model too would have poor predictive performance.

Can you cite some examples where both false positive and false negatives are equally important?

In the Banking industry giving loans is the primary source of making money but at the same time if your repayment rate is not good you will not make any profit, rather you will risk huge losses. Banks don't want to lose good customers and at the same point in time, they don't want to acquire bad customers. In this scenario, both the false positives and false negatives become very important to measure.

A certain couple tells you that they have two children, at least one of which is a girl. What is the probability that they have two girls?

In the case of two children, there are 4 equally likely possibilities BB, BG, GB and GG; where B = Boy and G = Girl and the first letter denotes the first child. From the question, we can exclude the first case of BB. Thus from the remaining 3 possibilities of BG, GB & BB, we have to find the probability of the case with two girls. Thus, P(Having two girls given one girl) = 1 / 3

Sampling selection bias:

It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.

What is Linear Regression?

Linear regression is a statistical technique where the score of a variable Y is predicted from the score of a second variable X. X is referred to as the predictor variable and Y as the criterion variable.

How do you decide how many clusters to use in a k-means algorithm?

Plot the sum of squares and find the elbow, use the x-values around the elbow to pick the number of clusters.

p-value

The probability of results of the experiment being attributed to chance. Usually want < .05.

What is a Boltzmann Machine?

They have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimize the weights and the quantity for the given problem. The learning algorithm is very slow in networks with many layers of feature detectors. "Restricted Boltzmann Machines" algorithm has a single layer of feature detectors which makes it faster than the rest.

How will you define the number of clusters in a clustering algorithm?

Though the Clustering Algorithm is not specified, this question is mostly in reference to K-Means clustering where "K" defines the number of clusters. The objective of clustering is to group similar entities in a way that the entities within a group are similar to each other but the groups are different from each other. For example, the following image shows three different groups. Clustering - Data Science Interview Questions - EdurekaWithin Sum of squares is generally used to explain the homogeneity within a cluster. If you plot WSS for a range of number of clusters, you will get the plot shown below. Clustering Plots - Data Science Interview Questions - Edureka The Graph is generally known as Elbow Curve. Red circled point in above graph i.e. Number of Cluster =6 is the point after which you don't see any decrement in WSS. This point is known as the bending point and taken as K in K - Means. This is the widely used approach but few data scientists also use Hierarchical clustering first to create dendrograms and identify the distinct groups from there.

Python or R - Which one would you prefer for text analytics?

We will prefer Python because of the following reasons: - Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools. - Python performs faster for all types of text analytics. - R is more suitable for machine learning than just text analysis.

F1 score and confusion matrix

What is the prediction accuracy metric for a multiclass classification model?

Data selection bias:

When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.

What is the difference between "long" and "wide" format data?

Wide: Name | Height | Weight John . | 160 . | 67 Chris . | 182 . | 78 Tall: Name| Attribute | Value John | Height . | 160 John . | Weight . | 67 Chris . | 182 . | 78

What is the role of Activation Function?

used to introduce non-linearity into the neural network helping it to learn more complex function. Without which the neural network would be only able to learn linear function which is a linear combination of its input data. An activation function is a function in an artificial neuron that delivers an output based on inputs

How can you generate a random number between 1 - 7 with only a die?

- Any die has six sides from 1-6. There is no way to get seven equal outcomes from a single rolling of a die. If we roll the die twice and consider the event of two rolls, we now have 36 different outcomes. - To get our 7 equal outcomes we have to reduce this 36 to a number divisible by 7. We can thus consider only 35 outcomes and exclude the other one. - A simple scenario can be to exclude the combination (6,6), i.e., to roll the die again if 6 appears twice. - All the remaining combinations from (1,1) till (6,5) can be divided into 7 parts of 5 each. This way all the seven sets of outcomes are equally likely.

Four types of selection bias include:

- Sampling bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample. - Time interval: A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean. - Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria. - Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants) discounting trial subjects/tests that did not run to completion.

What does a Data Scientist do?

- exploratory data analysis - machine learning and advanced algorithms - data product engineering

Accuracy

- how close a measurement is to the true value - the proportion of the total number of predictions that were correct. This score works best if false positives and false negatives have similar cost. If the cost of false positives and false negatives are very different, it's better to look at both Precision and Recall.

RMSE

- most popular evaluation metric used in regression problems, follows assumption that errors are unbiased and follow a normal distribution. - shows large number deviations. - aptly displays the plausible magnitude of error term. - avoids the use of absolute error values which is highly undesirable in mathematical calculations. When we have more samples, reconstructing the error distribution using this is considered to be more reliable. This is highly affected by outlier values. Hence, make sure you've removed outliers from your data set prior to using this metric. As compared to mean absolute error, this gives higher weightage and punishes large errors.

MSE

- the average of squared forecast errors - a more sensitive measure of forecast errors that approximates the error variance

Recall (Sensitivity)

- the proportion of actual positive cases which are correctly identified. - if this score is high, you didn't miss a lot of positives. But as it gets lower, you are not predicting the positives that are actually there. - a general practice doctor wants this high on tests to refer patients to a specialist - want this high on test diagnosing highly contagious and dangerous disease like Ebola. tp / (tp + fn)

Negative Predictive Value (NPV)

- the proportion of negative cases that were correctly identified. TN / (TN + FN)

Precision

- the proportion of positive cases that were correctly predicted. - the higher this number is, the more you were able to pinpoint all positives correctly. If this is a low score, you predicted a lot of positives where there were none. - ratio of correctly predicted positive observations to the total predicted positive observations. High precision relates to the low false positive rate. - A specialist wants high precision for expensive/invasive tests. tp / (tp + fp) (also Positive Predictive Value)

Foreign Key

A primary key of one table that appears as an attribute in another table and acts to provide a logical relationship between the two tables

Time interval selection bias

A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.

feature engineering

A way to create features or variables that are stronger predictors of your target variable

What is the F1 score used for?

Alternative measure of model's performance, is usually more useful than accuracy, especially if you have an uneven class distribution. Harmonic mean of precision and recall. 1 is best and 0 is worst. Used in classification where true negatives don't matter so much. this score takes both false positives and false negatives into account.

What are the various classification algorithms?

Classifier: - Linear: * Naive Bayes * Logistic Regression - Decision Trees - SVM * Linear * Non-linear - Kernel Estimation - Neural Networks * Recurrent neural network (RNN) * Modular Neural Network - Quadratic

What is Cluster Sampling?

Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample where each sampling unit is a collection or cluster of elements. For eg., A researcher wants to survey the academic performance of high school students in Japan. He can divide the entire population of Japan into different clusters (cities). Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling.

What methods are used to test the validity of our model?

Comparing the test and training data's accuracy, precision, recall, rmse, mse, mae, and cross validation

A model performs at 99% accuracy, what conditions could this happen under?

Completely imbalanced data. If 99/100 is 0, and the model predicts 0, it will be 99% correct. Decision tree - over fitting. Putting in a lot of clusters where it covers all the data points. Or one feature is a direct calculation of the other.

What is the difference between a regression and classification problem?

Continuous(regression) vs. discrete(classification)

Explain cross-validation.

Cross-validation is a model validation technique for evaluating how the outcomes of statistical analysis will generalize to an Independent dataset. Mainly used in backgrounds where the objective is forecast and one wants to estimate how accurately a model will accomplish in practice. The goal of cross-validation is to term a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting and get an insight on how the model will generalize to an independent data set.

What is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years? The answer lies in the difference between explaining and predicting.

How does data cleaning plays a vital role in analysis?

Data cleaning can help in analysis because: - Cleaning data from multiple sources helps to transform it into a format that data analysts or data scientists can work with. - Data Cleaning helps to increase the accuracy of the model in machine learning. - It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources. - It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

Normal Distribution

Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, this type of data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve. Properties of Normal Distribution: Unimodal -one mode Symmetrical -left and right halves are mirror images Bell-shaped -maximum height (mode) at the mean Mean, Mode, and Median are all located in the center Asymptotic

names of common classification algorithms

Decision Tree, logistic regression, K nearest neighbors, Support Vector Machines, etc.

What do you mean by Deep Learning and Why has it become popular now?

Deep Learning is nothing but a paradigm of machine learning which has shown incredible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the human brain. Now although Deep Learning has been around for many years, the major breakthroughs from these techniques came just in recent years. This is because of two main reasons: The increase in the amount of data generated through various sources The growth in hardware resources required to run these models GPUs are multiple times faster and they help us build bigger and deeper deep learning models in comparatively less time than we required previously

What are Eigenvectors and Eigenvalues?

Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching. Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.

Can you cite some examples where a false negative important than a false positive?

Example 1: Assume there is an airport 'A' which has received high-security threats and based on certain characteristics they identify whether a particular passenger can be a threat or not. Due to a shortage of staff, they decide to scan passengers being predicted as risk positives by their predictive model. What will happen if a true threat customer is being flagged as non-threat by airport model? Example 2: What if Jury or judge decides to make a criminal go free? Example 3: What if you rejected to marry a very good person based on your predictive model and you happen to meet him/her after a few years and realize that you had a false negative?

A/B Testing

It is a statistical hypothesis testing for a randomized experiment with two variables A and B. Web applications. Measuring click rate, effectiveness of campaigns for two different interfaces/models. A randomized group of experiments used to collect data and compare performance among two options studied (A and B).

Can you cite some examples where a false positive is important than a false negative?

Let us first understand what false positives and false negatives are. False Positives are the cases where you wrongly classified a non-event as an event a.k.a Type I error. False Negatives are the cases where you wrongly classify events as non-events, a.k.a Type II error. Example 1: In the medical field, assume you have to give chemotherapy to patients. Assume a patient comes to that hospital and he is tested positive for cancer, based on the lab prediction but he actually doesn't have cancer. This is a case of false positive. Here it is of utmost danger to start chemotherapy on this patient when he actually does not have cancer. In the absence of cancerous cell, chemotherapy will do certain damage to his normal healthy cells and might lead to severe diseases, even cancer. Example 2: Let's say an e-commerce company decided to give $1000 Gift voucher to the customers whom they assume to purchase at least $10,000 worth of items. They send free voucher mail directly to 100 customers without any minimum purchase condition because they assume to make at least 20% profit on sold items above $10,000. Now the issue is if we send the $1000 gift vouchers to customers who have not actually purchased anything but are marked as having made $10,000 worth of purchase.

What is Machine Learning?

Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. Closely related to computational statistics. Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics.

How can outlier values be treated?

Outlier values can be identified by using univariate or any other graphical analysis method. If the number of outlier values is few then they can be assessed individually but for a large number of outliers, the values can be substituted with either the 99th or the 1st percentile values. All extreme values are not outlier values. The most common ways to treat outlier values 1. To change the value and bring in within a range. 2. To just remove the value.

In any 15-minute interval, there is a 20% probability that you will see at least one shooting star. What is the probability that you see at least one shooting star in the period of an hour?

Probability of not seeing any shooting star in 15 minutes is = 1 - P( Seeing one shooting star ) = 1 - 0.2 = 0.8 Probability of not seeing any shooting star in the period of one hour = (0.8) ^ 4 = 0.4096 Probability of seeing at least one shooting star in the one hour = 1 - P( Not seeing any star ) = 1 - 0.4096 = 0.5904

Cross-validation

Process used with multiple regression techniques in which a regression equation developed on a first sample is tested on a second sample to determine if it still fits well; usually carried out with an incumbent sample, and the cross-validated results are used to weight the predictor scores of an applicant sample

What are Recommender Systems?

Recommender Systems are 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. Examples include movie recommenders in IMDB, Netflix & BookMyShow, product recommenders in e-commerce sites like Amazon, eBay & Flipkart, YouTube video recommendations and game recommendations in Xbox.

What do you understand by statistical power of sensitivity and how do you calculate it?

Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest 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 seasonality is pretty straightforward. Seasonality = ( True Positives ) / ( Positives in Actual Dependent Variable ) *where true positives are positive events which are correctly classified as positives.

What is logistic regression?

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 the variants of Back Propagation?

Stochastic Gradient Descent: We use only single training example for calculation of gradient and update parameters. Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at each iteration. Mini-batch Gradient Descent: It's one of the most popular optimization algorithms. It's a variant of Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is used.

What is the Supervised Learning?

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks E.g. If you built a fruit classifier, the labels will be "this is an orange, this is an apple and this is a banana", based on showing the classifier examples of apples, oranges and bananas.

What is Systematic Sampling?

Systematic sampling is a statistical technique where elements are selected from an ordered sampling frame. In systematic sampling, the list is progressed in a circular manner so once you reach the end of the list, it is progressed from the top again. The best example of systematic sampling is equal probability method.

F1 Score

The balanced harmonic mean of Recall and Precision, giving both metrics equal weight. The closer this score is to 1, the better. - usually more useful than accuracy, especially if you have an uneven class distribution. Accuracy works best if false positives and false negatives have similar cost. If the cost of false positives and false negatives are very different, it's better to look at both Precision and Recall. = 2*(Recall * Precision) / (Recall + Precision)

During analysis, how do you treat missing values?

The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights. If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored. Assigning a default value which can be mean, minimum or maximum value. Getting into the data is important. If it is a categorical variable, the default value is assigned. The missing value is assigned a default value. If you have a distribution of data coming, for normal distribution give the mean value. If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.

What is the goal of A/B Testing?

The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest. A/B testing is a fantastic method for figuring out the best online promotional and marketing strategies for your business. It can be used to test everything from website copy to sales emails to search ads An example of this could be identifying the click-through rate for a banner ad. A/B testing is often used in refining the design of technology products, and A/B tests are particularly easy to run over the Internet on a firm's Web site. Amazon, Google, and Facebook are among the firms that aggressively leverage hundreds of A/B tests a year in order to improve their product offerings.

What is Collaborative filtering?

The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents. An example of collaborative filtering can be to predict the rating of a particular user based on his/her ratings for other movies and others' ratings for all movies. This concept is widely used in recommending movies in IMDB, Netflix & BookMyShow, product recommenders in e-commerce sites like Amazon, eBay & Flipkart, YouTube video recommendations and game recommendations in Xbox.

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

Differentiate between univariate, bivariate and multivariate analysis.

Univariate analyses 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 the analysis can be referred to as univariate analysis. The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis. Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.

STD

a computed measure of how much scores vary around the mean score

Primary Key

a field that uniquely identifies a record in a table

What is Selection Bias?

a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn't random. It is sometimes referred to as the selection effect. It is the distortion of statistical analysis, resulting from the method of collecting samples. If the this error is not taken into account, then some conclusions of the study may not be accurate.

What are Artificial Neural Networks?

a specific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks. Neural Networks can adapt to changing input so the network generates the best possible result without needing to redesign the output criteria.

Accuracy vs. Precision

accuracy: ratio of correctly predicted observation to the total observations. One may think that, if we have high accuracy then our model is best. Yes, accuracy is a great measure but only when you have symmetric datasets where values of false positive and false negatives are almost same. Therefore, you have to look at other parameters to evaluate the performance of your model. precision: ratio of correctly predicted positive observations to the total predicted positive observations. High precision relates to the low false positive rate. An oncologist wants high precision for expensive/invasive tests.

What is an Auto-Encoder?

simple learning networks that aim to transform inputs into outputs with the minimum possible error. This means that we want the output to be as close to input as possible. We add a couple of layers between the input and the output, and the sizes of these layers are smaller than the input layer. The autoencoder receives unlabeled input which is then encoded to reconstruct the input.

Specificity

the proportion of actual negative cases which are correctly identified. TN / (TN + FP)

regression

used on continuous variables - measures the impact of a set of variables on another variable

classification

used on discrete variables - process of grouping observations based on their similarities

What are the different Deep Learning Frameworks?

- Pytorch - TensorFlow - Microsoft Cognitive Toolkit - Keras - Caffe - Chainer

What is Unsupervised learning?

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models E.g. In the same example, a fruit clustering will categorize as "fruits with soft skin and lots of dimples", "fruits with shiny hard skin" and "elongated yellow fruits".

Support

number of occurrences of each class where y is true.

IQR

(interquartile range) measure of statistical dispersion, being equal to the difference between the upper and lower quartiles, IQR = Q3 − Q1.

What is k-means?

A clustering method for unstructured data. K is the # of clusters.

Supervised Learning

1. Input data is labeled. 2. Uses training dataset. 3. Used for prediction. 4. Enables classification and regression.

Unsupervised Learning

1. Input data is unlabeled. 2. Uses the input data set. 3. Used for analysis. 4. Enables Classification, Density Estimation, & Dimension Reduction

What are the various steps involved in an analytics project?

1. Understand the Business problem 2. Explore the data and become familiar with it. 3. Prepare the data for modeling by detecting outliers, treating missing values, transforming variables, etc. 4. After data preparation, start running the model, analyze the result and tweak the approach. This is an iterative step until the best possible outcome is achieved. 5. Validate the model using a new data set. 6. Start implementing the model and track the result to analyze the performance of the model over the period of time.

What are the steps of the data science pipeline?

1. plan 2. acquire 3. preparation 4. explore 5. model

Can you explain the difference between a Validation Set and a Test Set?

A Validation set can be considered as a part of the training set as it is used for parameter selection and to avoid overfitting of the model being built. On the other hand, a Test Set is used for testing or evaluating the performance of a trained machine learning model. In simple terms, the differences can be summarized as; training set is to fit the parameters i.e. weights and test set is to assess the performance of the model i.e. evaluating the predictive power and generalization.

What is k-nearest neighbors?

A classification method for structured (labeled) data. K is the # of neighbors.

Attrition selection bias:

Attrition bias is a kind of selection bias caused by loss of participants, discounting trial subjects/tests that did not run to completion.

What is Back Propagation and Explain it's Working.

a training algorithm used for multilayer neural network. In this method, we move the error from an end of the network to all weights inside the network and thus allowing efficient computation of the gradient. It has the following steps: - Forward Propagation of Training Data - Derivatives are computed using output and target - Back Propagate for computing derivative of error wrt output activation - Using previously calculated derivatives for output - Update the Weights

What can we do to missing data/nans?

drop, ffill, bfill, regression, impute

Type 2 error

failing to reject a false null hypothesis (false negative)

OneHotEncoder

maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index. For example with 5 categories, an input value of 2.0 would map to an output vector of [0.0, 0.0, 1.0, 0.0]. The last category is not included by default (configurable via dropLast) because it makes the vector entries sum up to one, and hence linearly dependent. So an input value of 4.0 maps to [0.0, 0.0, 0.0, 0.0]. class pyspark.ml.feature.OneHotEncoder(dropLast=True, inputCol=None, outputCol=None)

Describe the structure of Artificial Neural Networks

work on the same principle as a biological Neural Network. It consists of inputs which get processed with weighted sums and Bias, with the help of Activation Functions.