Machine Learning Interview Questions and Answers
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Best Machine Learning Interview Questions and Answers
Check your Machine Learning skills by going through all the top 50 Machine Learning interview questions and answers. We have compiled almost every topic that is associated with Machine Learning. These Machine Learning interview questions and answers are listed as per the consultation with top interviews and employers in this field. We have also discussed with the candidates who recently attended the Machine Learning interviews and prepared the questions accordingly.
These top 50 Machine Learning interview questions and answers are compatible for both freshers who wish to start their career in the field of Machine Learning and professionals who wish to elevate their career journey. So, if you wish to attend Machine Learning interviews? Then do not miss reading all these 50 machine Learning interview and questions. These questions will surely help you to crack the interview and get placed in the top multinational companies with attractive salary package. We wish you all success in your Machine Learning preparation.
Top Machine Learning Interview Questions and Answers
Machine Learning is also similar to Artificial Intelligence that utilizes the process of system programming such that the data analysis process is completely automated which ensures computers to learn and perform based on the experiences rather than complex coding or programming.
Supervised Learning | Unsupervised Learning | Reinforcement Learning |
The machine acquires or grabs information or learns with the help of labelled data | Here, the machine is educated to perform using unlabelled data without any proper guidance or support | In reinforcement leaning, the environment is interacted by an agent that analysis rewards or issues that rise |
The types of problems found in supervised learning are classification and regression | The types of problems found in unsupervised learning are clustering and association | The problem associated with reinforcement learning is reward based |
The supervised learning actually maps the labelled input to the determined output | Unsupervised learning actually understands the available patterns and discovers an accurate output | Reinforcement learning undergoes the most advanced trial and error method |
External supervision is required in supervised learning | No supervision is required in unsupervised learning | No supervision is required in reinforcement learning |
Deep Learning | Machine Learning |
Deep learning is also a form of Machine learning which actually performs like a human brain and is also highly beneficial in accurately detecting features | Machine leaning is totally algorithm-oriented which parse data, understands, learns or grasps information from that data and then applies those condition learnt from that data to deliver particularly informed decisions |
Classification | Regression |
It can predict a discrete class label | It can be able to predict a continuous quantity |
A problem that consists of two classes is known as binary in classification and if it has more than two classes, then it can be determined as multi-class classification | While a problem that holds various input variables in regression can be termed as a multivariate regression problem |
The process of classifying spam and non-spam emails can be an example for classification problems | The process of predicting the stock’s price over a particular period of time can be an example for regression problems |
Overfitting arises when a particular model learns too much and takes up too much of concepts from the training data set which eventually affects the actual performance of the new data model.
Here are some of the techniques which can be used to avoid overfitting in Machine Learning:
- Make use of the right algorithm and simple model
- Make use of k-folds that comes of cross validation method
- In case any parameter of the model is found efficient in creating overfitting then penalize those parameters with the help of regularization technique
Training set | Test set |
The examples provided for the model to understand, learn and predict or analyze is known as the training set | Test set can be used to check the accuracy of the model’s hypothesis thus created |
Over 70% of the data can be considered to be as a training dataset | The left over 30% of data can be grouped as the test set |
It can also be referred to as the labelled data which can be proficiently used to train the model | Here labelled data is not required for testing but to examine the results labelled data is highly used |
Bias is termed to be difference among the average predictions of correct value and the model thus created. The model prediction will not be accurate if the bias value goes high so to get the desired result, the value of bias should always be as low as possible.
Selection bias is referred to as a statistical error which created a bias in a particular segment of an environment that is sampled. It makes one sampled segment to be selected frequently in an environment over the other segments. In case the selection bias is not determined, then the results produced will be inaccurate.
Variance is the prediction difference between the training set and the other training sets anticipated value. The output fluctuates, if the variance value goes high and so the variance value should be as low as possible.
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The table which can be effectively used to summarize classification algorithm’s performance is known as the confusion matrix which can also be defined as an error matrix.
The two parameters found in confusion matrix are namely actual and predicted.
Precision is declared to be the fraction of relevant or required instance among the particular received instances and the result of precision will always be positive.
Recall also known as sensitivity is declared to be the fraction of relevant instances which are retrieved or acquired from the total received instances.
Inductive Learning | Deductive Learning |
The process of drawing conclusions with the help of observations are known as inductive learning | The process of forming observations with the help of conclusions are known as deductive learning |
K-Nearest Neighbour | K-Means Clustering |
It is a supervised technique | It is an unsupervised technique |
It is mainly used for regression or classification | It is mainly used for clustering |
In KNN, the letter “K” actually refers to the number of nearest neighbours that are used in the case of continuous regression or variable to predict or classify | In K-Means, the letter “K” refers to the total number of clusters that are being searched by the algorithm to learn and understand from the available data |
The useful technique to handle the corrupted or missing data in a dataset is to either drop the rows or columns that consist of corrupted or missing data or to replace those corrupted or missed data with other required necessary data or value.
IsNull() and dropna() can be used to find and drop the rows or columns that consist of corrupted or missed data
Fillna() can be used to replace the corrupted or missed data with some other useful value or data
False Positive – The cases that are denoted as Positive (True) but are actually Negative (False)
False Negative – The cases that are denoted as Negative (False) but are actually Positive (True)
True Positive (TP) – When the condition is correctly predicted by the model, then the resultant value will be True Positive
True Negative (TN) – When the negative condition is correctly predicted by the model or class, then the resultant value will be True Negative
The three stages involved in creating a new model in Machine Learning are Model Building, Model Testing and Applying the Model.
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The advantages of supervised machine learning are as follows:
- Detecting spam email
- Diagnosing healthcare
- Analyzing sentiments
- Detecting fraudulent activities
Yes, actually labelled data is used by supervised machine learning and no training data are being used by unsupervised machine learning while in semi-supervised machine learning, the training data thus consists of a little labelled data and a huge volume of unlabelled data.
Listed below are some of the essential functionalities of supervised learning:
- Classification
- Speech Recognition
- Regression
- Predict Time Series
- Annotate Strings
Linear regression is actually an algorithm used in supervised machine learning to detect the linear relationship found between the independent and dependent variables to ensure accurate predictive analysis.
The measure of volume of multicollinearity present in a set of collection available in several regression variables are known as the variance inflation factor.
When the two predictor variables consist of the same correlation when present in a multiple regression, then it can denote the occurance of collinearity.
When more than two predictor variables which are highly inter-correlated, then it can denote the occurance of multicollineraity.
Type I Error | Type II Error |
It is False Positive | It is actually False Negative |
It is the process of claiming something that occurred when nothing happened | It is the process of claiming nothing while only something has particularly happened |
Clustering and Association are the two techniques involved in unsupervised machine learning.
The process that divides a set of data into its subsets called clusters is known as clustering. Each cluster consists of several data that reveals information about the object.
Naïve in Naïve Bayes classifier denotes the assumptions which may or may not be true or right.
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The algorithm in supervised machine learning to classify problems is known as the random forest. In the training phase, the random forest algorithm actually generates several decision trees and final decision will be based on the majority of the decision tree’s result.
Decision tree which uses both numerical and categorical data actually forms classification models which are similar to tree structure in which a single dataset will be divided into several small subsets in the form of a tree with various nodes and branches.
The technique which can reduce the decision tree size in machine learning is known as pruning. It can be used to decrease the final classifier complexity and can enhance the accuracy of predictions with the help of overfitting reduction.
The classification algorithm that is used to analyze a particular set of available independent variable and predict an outcome in the form of binary variables.
Logistic regression comes under three different types and they are Binary logistic regression, Multinomial logistic regression and Ordinal logistic regression.
ROC stands for Receiver Operating Characteristic Curve and is the principle tool that is used for evaluating diagnostic test. It is actually a graph for several cut-off points included in the diagnostic test that depicts the sensitivity known as true positive rate against specificity which can be defined as a false positive rate.
The subset of model performance is model accuracy and so both the accuracy and performance of the model is proportional directly and so if the performance of the model is better, then the accuracy and predictions of the model will also be better.
Both Gini Impurity and Entropy are used to decide on how a decision tree can be split.
Entropy | Information Gain |
To analyze the messing data, entropy can be used | Information gain is associated with the reduction in the entropy as the dataset is continuously split based on an attribute |
On marching closer to the leaf node, the value of the entropy will be decreased | On marching closer to the leaf node, the value of the information gain will be increased |
Eigenvalues – It is nothing but the scalar which can be used to perform transformation on Eigenvectors.
Eigenvectors – It is nothing but the vectors whose directions remains unchanged even after a linear transformation is executed on them.
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Dimensionality reduction is the process cutting down the irrelevant features that makes the task complex with the assistance of principal variables which are known to be the sub group of feature-rich parent variable.
Model selection is the process of identifying the models with the same data among several other mathematical models. Model selection is used in various processes like data mining, statistics and machine learning.
In ensemble learning, the process of bagging is to improve classification schemes and unstable estimation.
Boosting is the process which can be used to decrease the combined model’s bias.
The two main components that are involved in Bayesian logic program are namely logical and quantitative.
F1 score actually determines the performance measurement of any particular model. It can also be defined as the weighted average of both recall and precision of the model. When the score is 1, the result tends to be best while the result turns worst when the score is 0.
Underfitting in Machine Learning occurs when we determine only low errors in testing and training set.
Whenever the model starts to overfit or underfit, then in that case, the process of regularization becomes essential.
The standard approach in supervised learning actually splits an example into specific training and test set.
The major approaches used in Machine Learning are as follows:
- Concept Vs Classification Learning
- Symbolic Vs Statistical Learning
- Inductive Vs Analytical Learning
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