Machine Learning Interview Questions and Answers
Question - 11 : - What do you understand by Reinforcement Learning technique?
Answer - 11 : -
Reinforcement learning is an algorithm technique used in Machine Learning. It involves an agent that interacts with its environment by producing actions & discovering errors or rewards. Reinforcement learning is employed by different software and machines to search for the best suitable behavior or path it should follow in a specific situation. It usually learns on the basis of reward or penalty given for every action it performs.
Question - 12 : - What is the trade-off between bias and variance?
Answer - 12 : -
Both bias and variance are errors. Bias is an error due to erroneous or overly simplistic assumptions in the learning algorithm. It can lead to the model under-fitting the data, making it hard to have high predictive accuracy and generalize the knowledge from the training set to the test set.
Variance is an error due to too much complexity in the learning algorithm. It leads to the algorithm being highly sensitive to high degrees of variation in the training data, which can lead the model to overfit the data.
To optimally reduce the number of errors, we will need to tradeoff bias and variance.
Question - 13 : - How do classification and regression differ?
Answer - 13 : -
| Classification | Regression |
- Classification is the task to predict a discrete class label.
| - Regression is the task to predict a continuous quantity.
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- In a classification problem, data is labeled into one of two or more classes.
| - A regression problem needs the prediction of a quantity.
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- A classification having problem with two classes is called binary classification, and more than two classes is called multi-class classification
| - A regression problem containing multiple input variables is called a multivariate regression problem.
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- Classifying an email as spam or non-spam is an example of a classification problem.
| - Predicting the price of a stock over a period of time is a regression problem.
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Question - 14 : - What are the five popular algorithms we use in Machine Learning?
Answer - 14 : -
Five popular algorithms are:
- Decision Trees
- Probabilistic Networks
- Neural Networks
- Support Vector Machines
- Nearest Neighbor
Question - 15 : - What do you mean by ensemble learning?
Answer - 15 : -
Numerous models, such as classifiers are strategically made and combined to solve a specific computational program which is known as ensemble learning. The ensemble methods are also known as committee-based learning or learning multiple classifier systems. It trains various hypotheses to fix the same issue. One of the most suitable examples of ensemble modeling is the random forest trees where several decision trees are used to predict outcomes. It is used to improve the classification, function approximation, prediction, etc. of a model.
Question - 16 : - What is a model selection in Machine Learning?
Answer - 16 : -
The process of choosing models among diverse mathematical models, which are used to define the same data is known as Model Selection. Model learning is applied to the fields of statistics, data mining, and machine learning.
Question - 17 : - What are the three stages of building the hypotheses or model in machine learning?
Answer - 17 : -
There are three stages to build hypotheses or model in machine learning:
Model building
It chooses a suitable algorithm for the model and trains it according to the requirement of the problem.
Applying the model
It is responsible for checking the accuracy of the model through the test data.
Model testing
It performs the required changes after testing and apply the final model.
Question - 18 : - What according to you, is the standard approach to supervised learning?
Answer - 18 : -
In supervised learning, the standard approach is to split the set of example into the training set and the test.
Question - 19 : - Describe 'Training set' and 'training Test'.
Answer - 19 : -
In various areas of information of machine learning, a set of data is used to discover the potentially predictive relationship, which is known as 'Training Set'. The training set is an example that is given to the learner. Besides, the 'Test set' is used to test the accuracy of the hypotheses generated by the learner. It is the set of instances held back from the learner. Thus, the training set is distinct from the test set.
Question - 20 : - What are the common ways to handle missing data in a dataset?
Answer - 20 : -
Missing data is one of the standard factors while working with data and handling. It is considered as one of the greatest challenges faced by the data analysts. There are many ways one can impute the missing values. Some of the common methods to handle missing data in datasets can be defined as deleting the rows, replacing with mean/median/mode, predicting the missing values, assigning a unique category, using algorithms that support missing values, etc.