Top 50 Machine Learning Interview Questions and Answers

Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience.  For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.

Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed.  While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns.  During this process machine, learning algorithms are used.

In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘over-fitting’ occurs.  When a model is excessively complex, over-fitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been over-fit.

The possibility of over-fitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.

By using a lot of data over-fitting can be avoided, over-fitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the data-points will come up with the model.

In this technique,  a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.

The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

The different types of techniques in Machine Learning are

The standard approach to supervised learning is to split the set of example into the training set and the test.

In various areas of information science like machine learning, a set of data is used to discover the potentially predictive relationship known as ‘Training Set’. Training set is an examples given to the learner, while Test set is used to test the accuracy of the hypotheses generated by the learner, and it is the set of example held back from the learner. Training set are distinct from Test set.

The different approaches in Machine Learning are

Machine learning in where mathematical foundations is independent of any particular classifier or learning algorithm is referred as algorithm independent machine learning?

Designing and developing algorithms according to the behaviors based on empirical data are known as Machine Learning.  While artificial intelligence in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc.

A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.

In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data.  The main advantage is that it can’t learn interactions between features.

Pattern Recognition can be used in

Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.

Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming representing background knowledge and examples.

The process of selecting models among different mathematical models, which are used to describe the same data set is known as Model Selection. Model selection is applied to the fields of statistics, machine learning and data mining.

The two methods used for predicting good probabilities in Supervised Learning are

These methods are designed for binary classification, and it is not trivial.

When there is sufficient data ‘Isotonic Regression’ is used to prevent an overfitting issue.

The difference is that the heuristics for decision trees evaluate the average quality of a number of disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with the candidate rule.

In Machine Learning, Perceptron is an algorithm for supervised classification of the input into one of several possible non-binary outputs.

Bayesian logic program consists of two components.  The first component is a logical one ; it consists of a set of Bayesian Clauses, which captures the qualitative structure of the domain.  The second component is a quantitative one, it encodes the quantitative information about the domain.

Bayesian Network is used to represent the graphical model for probability relationship among a set of variables.

Instance based learning algorithm is also referred as Lazy learning algorithm as they delay the induction or generalization process until classification is performed.

To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined. This process is known as ensemble learning.

Ensemble learning is used to improve the classification, prediction, function approximation etc of a model.

Ensemble learning is used when you build component classifiers that are more accurate and independent from each other.

The two paradigms of ensemble methods are

The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm in order to improve robustness over a single model.  Bagging is a method in ensemble for improving unstable estimation or classification schemes.  While boosting method are used sequentially to reduce the bias of the combined model.  Boosting and Bagging both can reduce errors by reducing the variance term.

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function.  The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.

Incremental learning method is the ability of an algorithm to learn from new data that may be available after classifier has already been generated from already available dataset.

PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.

In Machine Learning and statistics, dimension reduction is the process of reducing the number of random variables under considerations and can be divided into feature selection and feature extraction.

Support vector machines are supervised learning algorithms used for classification and regression analysis.

The important components of relational evaluation techniques are

The different methods to solve Sequential Supervised Learning problems are

The areas in robotics and information processing  where sequential prediction problem arises are

Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.

PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.

Sequence learning is a method of teaching and learning in a logical manner.

The two techniques of Machine Learning are

The recommendation engine implemented by major eCommerce websites uses Machine Learning.