A Machine Learning interview calls for a rigorous interview process where the candidates are judged on various aspects such as technical and programming skills, knowledge of methods and clarity of basic concepts. Here, we have compiled a list of machine learning interview questions that you might face during an interview.

**1. What are the different types of Learning/ Training models in ML?**

ML algorithms can be primarily classified depending on the presence/absence of target variables.

** A. Supervised learning:** [Target is present]

The machine learns using labelled data. The model is trained on an existing data set before it starts making decisions with the new data.

The target variable is continuous

*:*Linear Regression, polynomial Regression, quadratic Regression.

The target variable is categorical

*:*Logistic regression, Naive Bayes, KNN, SVM, Decision Tree, Gradient Boosting, ADA boosting, Bagging, Random forest etc.

* B. Unsupervised learning:* [Target is absent]

The machine is trained on unlabelled data and without any proper guidance. It automatically infers patterns and relationships in the data by creating clusters. The model learns through observations and deduced structures in the data.

Principal component Analysis, Factor analysis, Singular Value Decomposition etc.

**C. Reinforcement Learning:**

The model learns through a trial and error method. This kind of learning involves an agent that will interact with the environment to create actions and then discover errors or rewards of that action.

**2. What is the difference between deep learning and machine learning?**

Machine Learning involves algorithms that learn from patterns of data and then apply it to decision making. Deep Learning, on the other hand, is able to learn through processing data on its own and is quite similar to the human brain where it identifies something, analyse it, and makes a decision.

The key differences are as follow:

- The manner in which data is presented to the system.
- Machine learning algorithms always require structured data and deep learning networks rely on layers of artificial neural networks.

**3. How do you select important variables while working on a data set? **

There are various means to select important variables from a data set that include the following:

- Identify and discard correlated variables before finalizing on important variables
- The variables could be selected based on ‘p’ values from Linear Regression
- Forward, Backward, and Stepwise selection
- Lasso Regression
- Random Forest and plot variable chart
- Top features can be selected based on information gain for the available set of features.

**4. How are covariance and correlation different from one another?**

Covariance measures how two variables are related to each other and how one would vary with respect to changes in the other variable. If the value is positive it means there is a direct relationship between the variables and one would increase or decrease with an increase or decrease in the base variable respectively, given that all other conditions remain constant.

Correlation quantifies the relationship between two random variables and has only three specific values, i.e., 1, 0, and -1.

1 denotes a positive relationship, -1 denotes a negative relationship, and 0 denotes that the two variables are independent of each other.

**5. When does regularization come into play in Machine Learning?**

At times when the model begins to underfit or overfit, regularization becomes necessary. It is a regression that diverts or regularizes the coefficient estimates towards zero. It reduces flexibility and discourages learning in a model to avoid the risk of overfitting. The model complexity is reduced and it becomes better at predicting.

**6. What is Bias, Variance and what do you mean by Bias-Variance Tradeoff?**

Both are errors in Machine Learning Algorithms. When the algorithm has limited flexibility to deduce the correct observation from the dataset, it results in bias. On the other hand, variance occurs when the model is extremely sensitive to small fluctuations.

If one adds more features while building a model, it will add more complexity and we will lose bias but gain some variance. In order to maintain the optimal amount of error, we perform a tradeoff between bias and variance based on the needs of a business.

**7. How can we relate standard deviation and variance? **

*Standard deviation* refers to the spread of your data from the mean. *Variance* is the average degree to which each point differs from the mean i.e. the average of all data points. We can relate Standard deviation and Variance because it is the square root of Variance.

**8. Is a high variance in data good or bad? **

Higher variance directly means that the data spread is big and the feature has a variety of data. Usually, high variance in a feature is seen as not so good quality.

**9. What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?**

Gradient Descent and Stochastic Gradient Descent are the algorithms that find the set of parameters that will minimize a loss function.

The difference is that in Gradient Descend, all training samples are evaluated for each set of parameters. While in Stochastic Gradient Descent only one training sample is evaluated for the set of parameters identified.

**10. Can you mention some advantages and disadvantages of decision trees?**

The advantages of decision trees are that they are easier to interpret, are nonparametric and hence robust to outliers, and have relatively few parameters to tune.

On the other hand, the disadvantage is that they are prone to overfitting.

**11. What is the Principle Component Analysis?**

The idea here is to reduce the dimensionality of the data set by reducing the number of variables that are correlated with each other. Although the variation needs to be retained to the maximum extent.

The variables are transformed into a new set of variables that are known as Principal Components’. These PCs are the eigenvectors of a covariance matrix and therefore are orthogonal.

**12. What are outliers? Mention three methods to deal with outliers.**

A data point that is considerably distant from the other similar data points is known as an outlier. They may occur due to experimental errors or variability in measurement. They are problematic and can mislead a training process, which eventually results in longer training time, inaccurate models, and poor results.

The three methods to deal with outliers are:**Univariate method** – looks for data points having extreme values on a single variable**Multivariate method** – looks for unusual combinations on all the variables**Minkowski error** – reduces the contribution of potential outliers in the training process

Also, Read - Advantages of pursuing a career in Machine Learning

**13. What is the difference between regularization and normalisation? **

Normalisation adjusts the data; regularisation adjusts the prediction function. If your data is on very different scales (especially low to high), you would want to normalise the data. Alter each column to have compatible basic statistics. This can be helpful to make sure there is no loss of accuracy. One of the goals of model training is to identify the signal and ignore the noise if the model is given free rein to minimize error, there is a possibility of suffering from overfitting. Regularization imposes some control on this by providing simpler fitting functions over complex ones.

**14. List the most popular distribution curves along with scenarios where you will use them in an algorithm.**

The most popular distribution curves are as follows- Bernoulli Distribution, Uniform Distribution, Binomial Distribution, Normal Distribution, Poisson Distribution, and Exponential Distribution.

Each of these distribution curves is used in various scenarios.

Bernoulli Distribution can be used to check if a team will win a championship or not, a newborn child is either male or female, you either pass an exam or not, etc.

** Uniform distribution** is a probability distribution that has a constant probability. Rolling a single dice is one example because it has a fixed number of outcomes.

* Binomial distribution* is a probability with only two possible outcomes, the prefix ‘bi’ means two or twice. An example of this would be a coin toss. The outcome will either be heads or tails.

** Normal distribution** describes how the values of a variable are distributed. It is typically a symmetric distribution where most of the observations cluster around the central peak. The values further away from the mean taper off equally in both directions. An example would be the height of students in a classroom.

** Poisson distribution** helps predict the probability of certain events happening when you know how often that event has occurred. It can be used by businessmen to make forecasts about the number of customers on certain days and allows them to adjust supply according to the demand.

** Exponential distribution** is concerned with the amount of time until a specific event occurs. For example, how long a car battery would last, in months.

**15. How do we check the normality of a data set or a feature? **

Visually, we can check it using plots. There is a list of Normality checks, they are as follow:

- Shapiro-Wilk W Test
- Anderson-Darling Test
- Martinez-Iglewicz Test
- Kolmogorov-Smirnov Test
- D’Agostino Skewness Test

**16. What is Linear Regression?**

Linear Function can be defined as a Mathematical function on a 2D plane as, Y =Mx +C, where Y is a dependent variable and X is Independent Variable, C is Intercept and M is slope and same can be expressed as Y is a Function of X or Y = F(x).

At any given value of X, one can compute the value of Y, using the equation of Line. This relation between Y and X, with a degree of the polynomial as 1 is called Linear Regression.

In Predictive Modeling, LR is represented as Y = Bo + B1x1 + B2x2

The value of B1 and B2 determines the strength of the correlation between features and the dependent variable.

Example: Stock Value in $ = Intercept + (+/-B1)*(Opening value of Stock) + (+/-B2)*(Previous Day Highest value of Stock)

**17. Differentiate between regression and classification.**

Regression and classification are categorized under the same umbrella of supervised machine learning. The main difference between them is that the output variable in the regression is numerical (or continuous) while that for classification is categorical (or discrete).

Example: To predict the definite Temperature of a place is Regression problem whereas predicting whether the day will be Sunny cloudy or there will be rain is a case of classification.

**18. What is target imbalance? How do we fix it? A scenario where you have performed target imbalance on data. Which metrics and algorithms do you find suitable to input this data onto? **

If you have categorical variables as the target when you cluster them together or perform a frequency count on them if there are certain categories which are more in number as compared to others by a very significant number. This is known as the target imbalance.

Example: Target column – 0,0,0,1,0,2,0,0,1,1 [0s: 60%, 1: 30%, 2:10%] 0 are in majority. To fix this, we can perform up-sampling or down-sampling. Before fixing this problem let’s assume that the performance metrics used was confusion metrics. After fixing this problem we can shift the metric system to AUC: ROC. Since we added/deleted data [up sampling or downsampling], we can go ahead with a stricter algorithm like SVM, Gradient boosting or ADA boosting.

**19. List all assumptions for data to be met before starting with linear regression.**

Before starting linear regression, the assumptions to be met are as follow:

- Linear relationship
- Multivariate normality
- No or little multicollinearity
- No auto-correlation
- Homoscedasticity

**20. When does the linear regression line stop rotating or finds an optimal spot where it is fitted on data? **

A place where the highest RSquared value is found, is the place where the line comes to rest. RSquared represents the amount of variance captured by the virtual linear regression line with respect to the total variance captured by the dataset.

**21. Why is logistic regression a type of classification technique and not a regression? Name the function it is derived from? **

Since the target column is categorical, it uses linear regression to create an odd function that is wrapped with a log function to use regression as a classifier. Hence, it is a type of classification technique and not a regression. It is derived from cost function.

**22. Which machine learning algorithm is known as the lazy learner and why is it called so?**

KNN is a Machine Learning algorithm known as a lazy learner. K-NN is a lazy learner because it doesn’t learn any machine learnt values or variables from the training data but dynamically calculates distance every time it wants to classify, hence memorises the training dataset instead.

**23. Is it possible to use KNN for image processing? **

Yes, it is possible to use KNN for image processing. It can be done by converting the 3-dimensional image into a single-dimensional vector and using the same as input to KNN.

**24. Differentiate between K-Means and KNN algorithms?**

KNN is Supervised Learning where-as K-Means is Unsupervised Learning. With KNN, we predict the label of the unidentified element based on its nearest neighbour and further extend this approach for solving classification/regression-based problems.

K-Means is Unsupervised Learning, where we don’t have any Labels present, in other words, no Target Variables and thus we try to cluster the data based upon their coordinates and try to establish the nature of the cluster based on the elements filtered for that cluster.

**25. How does the SVM algorithm deal with self-learning? **

SVM has a learning rate and expansion rate which takes care of this. The learning rate compensates or penalises the hyperplanes for making all the wrong moves and expansion rate deals with finding the maximum separation area between classes.

**26. What are Kernels in SVM? List popular kernels used in SVM along with a scenario of their applications.**

The function of kernel is to take data as input and transform it into the required form. A few popular Kernels used in SVM are as follows: RBF, Linear, Sigmoid, Polynomial, Hyperbolic, Laplace, etc.

**27. What is Kernel Trick in an SVM algorithm?**

Kernel Trick is a mathematical function which when applied on data points, can find the region of classification between two different classes. Based on the choice of function, be it linear or radial, which purely depends upon the distribution of data, one can build a classifier.

**28. Explain how ensemble techniques yield better learning as compared to traditional classification ML algorithms? **

Ensemble is a group of models that are used together for prediction both in classification and regression class. Ensemble learning helps improve ML results because it combines several models. By doing so, it allows a better predictive performance compared to a single model.

They are superior to individual models as they reduce variance, average out biases, and have lesser chances of overfitting.

**29. What are overfitting and underfitting? Why does the decision tree algorithm suffer often with overfitting problem?**

Overfitting is a statistical model or machine learning algorithm which captures the noise of the data. Underfitting is a model or machine learning algorithm which does not fit the data well enough and occurs if the model or algorithm shows low variance but high bias.

In decision trees, overfitting occurs when the tree is designed to perfectly fit all samples in the training data set. This results in branches with strict rules or sparse data and affects the accuracy when predicting samples that aren’t part of the training set.

**30. What is OOB error and how does it occur? **

For each bootstrap sample, there is one-third of data that was not used in the creation of the tree, i.e., it was out of the sample. This data is referred to as out of bag data. In order to get an unbiased measure of the accuracy of the model over test data, out of bag error is used. The out of bag data is passed for each tree is passed through that tree and the outputs are aggregated to give out of bag error. This percentage error is quite effective in estimating the error in the testing set and does not require further cross-validation.

**31. Why boosting is a more stable algorithm as compared to other ensemble algorithms? **

Boosting focuses on errors found in previous iterations until they become obsolete. Whereas in bagging there is no corrective loop. This is why boosting is a more stable algorithm compared to other ensemble algorithms.

**32. List popular cross validation techniques.**

There are mainly six types of cross validation techniques. They are as follow:

- K fold
- Stratified k fold
- Leave one out
- Bootstrapping
- Random search cv
- Grid search cv

**33. Is it possible to test for the probability of improving model accuracy without cross validation techniques? If yes, please explain.**

Yes, it is possible to test for the probability of improving model accuracy without cross validation techniques. We can do so by running the ML model for say **n** number of iterations, recording the accuracy. Plot all the accuracies and remove the 5% of low probability values. Measure the left [low] cut off and right [high] cut off. With the remaining 95% confidence, we can say that the model can go as low or as high [as mentioned within cut off points].

**34. Name a popular dimensionality reduction algorithm.**

Popular dimensionality reduction algorithms are Principal Component Analysis and Factor Analysis.

Principal Component Analysis creates one or more index variables from a larger set of measured variables. Factor Analysis is a model of the measurement of a latent variable. This latent variable cannot be measured with a single variable and is seen through a relationship it causes in a set of** y** variables.

**35. How can we use a dataset without the target variable into supervised learning algorithms? **

Input the data set into a clustering algorithm, generate optimal clusters, label the cluster numbers as the new target variable. Now, the dataset has independent and target variables present. This ensures that the dataset is ready to be used in supervised learning algorithms.

**36. List all types of popular recommendation systems? Name and explain two personalised recommendation systems along with their ease of implementation. **

Popularity based recommendation, content-based recommendation, user-based collaborative filter, and item-based recommendation are the popular types of recommendation systems.

Personalised Recommendation systems are- Content-based recommendation, user-based collaborative filter, and item-based recommendation. User-based collaborative filter and item-based recommendations are more personalised. Ease to maintain: Similarity matrix can be maintained easily with Item-based recommendation.

**37. How do we deal with sparsity issues in recommendation systems? How do we measure its effectiveness? Explain. **

Singular value decomposition can be used to generate prediction matrix. RMSE is the measure that helps us understand how close the prediction matrix is to the original matrix.

**38. Name and define techniques used to find similarities in the recommendation system. **

Pearson correlation and Cosine correlation are techniques used to find similarities in recommendation systems.

**39. Keeping train and test split criteria in mind, is it good to perform scaling before the split or after the split? **

Scaling should be done post-train and test split ideally. If the data is closely packed, then scaling post or pre-split should not make much difference.

**40. Define precision, recall and F1 Score?**

The metric used to access the performance of the classification model is Confusion Metric. Confusion Metric can be further interpreted with the following terms:-

**True Positives (TP)** – These are the correctly predicted positive values. It implies that the value of the actual class is yes and the value of the predicted class is also yes.

**True Negatives (TN)** – These are the correctly predicted negative values. It implies that the value of the actual class is no and the value of the predicted class is also no.

**False positives and false negatives**, these values occur when your actual class contradicts with the predicted class.

**Now,****Recall,** also known as Sensitivity is the ratio of true positive rate (TP), to all observations in actual class – yes

Recall = TP/(TP+FN)

**Precision** is the ratio of positive predictive value, which measures the amount of accurate positives model predicted viz a viz number of positives it claims.

Precision = TP/(TP+FP)

**Accuracy** is the most intuitive performance measure and it is simply a ratio of correctly predicted observation to the total observations.

Accuracy = (TP+TN)/(TP+FP+FN+TN)

**F1 Score** is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. Accuracy works best if false positives and false negatives have a similar cost. If the cost of false positives and false negatives are very different, it’s better to look at both Precision and Recall.

**41. What is Bayes’ Theorem? State at least 1 use case with respect to the machine learning context?**

Bayes’ Theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if cancer is related to age, then, using Bayes’ theorem, a person’s age can be used to more accurately assess the probability that they have cancer than can be done without the knowledge of the person’s age.

Chain rule for Bayesian probability can be used to predict the likelihood of the next word in the sentence.

**42. Explain the difference between Lasso and Ridge?**

Lasso(L1) and Ridge(L2) are the regularization techniques where we penalize the coefficients to find the optimum solution. In ridge, the penalty function is defined by the sum of the squares of the coefficients and for the Lasso, we penalize the sum of the absolute values of the coefficients. Another type of regularization method is ElasticNet, it is a hybrid penalizing function of both lasso and ridge.

**43. What’s the difference between probability and likelihood?**

Probability is the measure of the likelihood that an event will occur that is, what is the certainty that a specific event will occur? Where-as a likelihood function is a function of parameters within the parameter space that describes the probability of obtaining the observed data.

So the fundamental difference is, Probability attaches to possible results; likelihood attaches to hypotheses.

**44. Why would you Prune your tree?**

Decision Trees are prone to overfitting, pruning the tree helps to reduce the size and minimizes the chances of overfitting. It serves as a tool to perform the tradeoff.

**45. Model accuracy or Model performance? Which one will you prefer and why?**

This is a trick question, one should first get a clear idea, what is Model Performance? If Performance means speed, then it depends upon the nature of the application, any application related to the real-time scenario will need high speed as an important feature. Example: The best of Search Results will lose its virtue if the Query results do not appear fast.

If Performance is hinted at Why Accuracy is not the most important virtue – For any imbalanced data set, more than Accuracy, it will be an F1 score than will explain the business case and in case data is imbalanced, then Precision and Recall will be more important than rest.

**46. How would you handle an imbalanced dataset?**

Sampling Techniques can help with an imbalanced dataset. There are two ways to perform sampling, Under Sample or Over Sampling.

In Under Sampling, we reduce the size of the majority class to match minority class thus help by improving performance w.r.t storage and run-time execution, but it potentially discards useful information.

For Over Sampling, we upsample the Minority class and thus solve the problem of information loss, however, we get into the trouble of having Overfitting.

There are other techniques as well –**Cluster-Based Over Sampling **– In this case, the K-means clustering algorithm is independently applied to minority and majority class instances. This is to identify clusters in the dataset. Subsequently, each cluster is oversampled such that all clusters of the same class have an equal number of instances and all classes have the same size

**Synthetic Minority Over-sampling Technique (SMOTE) – **A subset of data is taken from the minority class as an example and then new synthetic similar instances are created which are then added to the original dataset. This technique is good for Numerical data points.

**47. Mention some of the EDA Techniques?**

Exploratory Data Analysis (EDA) helps analysts to understand the data better and forms the foundation of better models.

**Visualization**

- Univariate visualization
- Bivariate visualization
- Multivariate visualization

**Missing Value Treatment** – Replace missing values with Either Mean/Median

**Outlier Detection** – Use Boxplot to identify the distribution of Outliers, then Apply IQR to set the boundary for IQR

**Transformation** – Based on the distribution, apply a transformation on the features

**Scaling the Dataset** – Apply MinMax, Standard Scaler or Z Score Scaling mechanism to scale the data.

**Feature Engineering** – Need of the domain, and SME knowledge helps Analyst find derivative fields which can fetch more information about the nature of the data

**Dimensionality reduction** — Helps in reducing the volume of data without losing much information

**48. Differentiate between Statistical Modeling and Machine Learning?**

Machine learning models are about making accurate predictions about the situations, like Foot Fall in restaurants, Stock-Price, etc. where-as, Statistical models are designed for inference about the relationships between variables, as What drives the sales in a restaurant, is it food or Ambience.

**49. Differentiate between Boosting and Bagging?**

Bagging and Boosting are variants of Ensemble Techniques, in Bagging, the goal is to reduce the variance of a decision tree classifier create several subsets of data from the training sample chosen randomly with replacement. Each collection of subset data is used to train their decision trees.

Example – Random Forest Classifier

In Boosting, learners are learned sequentially with early learners fitting simple models to the data and then analyzing data for errors. Consecutive trees (random sample) are fit and at every step, the goal is to improve the accuracy from the prior tree. When an input is misclassified by a hypothesis, its weight is increased so that the next hypothesis is more likely to classify it correctly. This process converts weak learners into a better performing model.

Example – AdaBoosting, Gradient Boosting, XGboosting

**50. What is the significance of Gamma and Regularization in SVM?**

The gamma defines influence. Low values meaning ‘far’ and high values meaning ‘close’. If gamma is too large, the radius of the area of influence of the support vectors only includes the support vector itself and no amount of regularization with C will be able to prevent overfitting. If gamma is very small, the model is too constrained and cannot capture the complexity of the data.

The regularization parameter (lambda) serves as a degree of importance that is given to miss-classifications. This can be used to draw the tradeoff with OverFitting.

*Stay tuned to this page for more such information on interview questions and career assistance. You can check our other blogs about Machine Learning for more information.**If you want to prepare more to land your dream job in ML, you can upskill with Great Learning’s PG program in Machine Learning. *

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