Machine Learning Interview Questions and Answers
Question - 51 : - Explain Machine Learning, Artificial Intelligence, and Deep Learning
Answer - 51 : -
It is common to get confused between the three in-demand technologies, Machine Learning, Artificial Intelligence, and Deep Learning. These three technologies, though a little different from one another, are interrelated. While Deep Learning is a subset of Machine Learning, Machine Learning is a subset of Artificial Intelligence. Since some terms and techniques may overlap in these technologies, it is easy to get confused among them.
So, let us learn about these technologies in detail:
- Machine Learning: Machine Learning involves various statistical and Deep Learning techniques that allow machines to use their past experiences and get better at performing specific tasks without having to be monitored.
- Artificial Intelligence: Artificial Intelligence uses numerous Machine Learning and Deep Learning techniques that enable computer systems to perform tasks using human-like intelligence with logic and rules.
- Deep Learning: Deep Learning comprises several algorithms that enable software to learn from themselves and perform various business tasks including image and speech recognition. Deep Learning is possible when systems expose their multilayered neural networks to large volumes of data for learning.
Question - 52 : - What is Bias and Variance in Machine Learning?
Answer - 52 : -
- Bias is the difference between the average prediction of a model and the correct value of the model. If the bias value is high, then the prediction of the model is not accurate. Hence, the bias value should be as low as possible to make the desired predictions.
- Variance is the number that gives the difference of prediction over a training set and the anticipated value of other training sets. High variance may lead to large fluctuation in the output. Therefore, a model’s output should have low variance.
The following diagram shows the bias-variance trade-off:
Here, the desired result is the blue circle at the center. If we get off from the blue section, then the prediction goes wrong.
Question - 53 : - What is Clustering in Machine Learning?
Answer - 53 : -
Clustering is a technique used in unsupervised learning that involves grouping data points. The clustering algorithm can be used with a set of data points. This technique will allow you to classify all data points into their particular groups. The data points that are thrown into the same category have similar features and properties, while the data points that belong to different groups have distinct features and properties. Statistical data analysis can be performed by this method. Let us take a look at three of the most popular and useful clustering algorithms.
- K-means clustering: This algorithm is commonly used when there is data with no specific group or category. K-means clustering allows you to find the hidden patterns in the data, which can be used to classify the data into various groups. The variable k is used to represent the number of groups the data is divided into, and the data points are clustered using the similarity of features. Here, the centroids of the clusters are used for labeling new data.
- Mean-shift clustering: The main aim of this algorithm is to update the center-point candidates to be mean and find the center points of all groups. In mean-shift clustering, unlike k-means clustering, the possible number of clusters need not be selected as it can automatically be discovered by the mean shift.
- Density-based spatial clustering of applications with noise (DBSCAN): This clustering algorithm is based on density and has similarities with mean-shift clustering. There is no need to preset the number of clusters, but unlike mean-shift clustering, DBSCAN identifies outliers and treats them like noise. Moreover, it can identify arbitrarily-sized and -shaped clusters without much effort.
Question - 54 : - What is Linear Regression in Machine Learning?
Answer - 54 : -
Linear Regression is a supervised Machine Learning algorithm. It is used to find the linear relationship between the dependent and independent variables for predictive analysis.
The equation for Linear Regression:
where:
- X is the input or independent variable
- Y is the output or dependent variable
- a is the intercept, and b is the coefficient of X
Below is the best-fit line that shows the data of weight, Y or the dependent variable, and the
ata of height, X or the independent variable, of 21-year-old candidates scattered over the plot. The straight line shows the best linear relationship that would help in predicting the weight of candidates according to their height.
To get this best-fit line, the best values of a and b should be found. By adjusting the values of a and b, the errors in the prediction of Y can be reduced.
Question - 55 : - What is Overfitting in Machine Learning and how can it be avoided?
Answer - 55 : -
Overfitting happens when a machine has an inadequate dataset and tries to learn from it. So, overfitting is inversely proportional to the amount of data.
For small databases, overfitting can be bypassed by the cross-validation method. In this approach, a dataset is divided into two sections. These two sections will comprise the testing and training dataset. To train a model, the training dataset is used, and for testing the model for new inputs, the testing dataset is used.
Question - 56 : - What is Hypothesis in Machine Learning?
Answer - 56 : -
Machine Learning allows the use of available dataset to understand a specific function that maps input to output in the best possible way. This problem is known as function approximation. Here, approximation needs to be used for the unknown target function that maps all plausible observations based on the given problem in the best manner. Hypothesis in Machine learning is a model that helps in approximating the target function and performing the necessary input-to-output mappings. The choice and configuration of algorithms allow defining the space of plausible hypotheses that may be represented by a model.
In the hypothesis, lowercase h (h) is used for a specific hypothesis, while uppercase h (H) is used for the hypothesis space that is being searched. Let us briefly understand these notations:
- Hypothesis (h): A hypothesis is a specific model that helps in mapping input to output; the mapping can further be used for evaluation and prediction.
- Hypothesis set (H): Hypothesis set consists of a space of hypotheses that can be used to map inputs to outputs, which can be searched. The general constraints include the choice of problem framing, the model, and the model configuration.
Question - 57 : - What are the differences between Deep Learning and Machine Learning?
Answer - 57 : -
- Deep Learning: Deep Learning allows machines to make various business-related decisions using artificial neural networks, which is one of the reasons why it needs a vast amount of data for training. Since there is a lot of computing power required, Deep Learning requires high-end systems as well. The systems acquire various properties and features with the help of the given data, and the problem is solved using an end-to-end method.
- Machine Learning: Machine Learning gives machines the ability to make business decisions without any external help, using the knowledge gained from past data. Machine Learning systems require relatively small amounts of data to train themselves, and most of the features need to be manually coded and understood in advance. In Machine Learning, a given business problem is dissected into two and then solved individually. Once the solutions of both have been acquired, they are then combined.
Question - 58 : - What are the differences between Supervised and Unsupervised Machine Learning?
Answer - 58 : -
Supervised learning: The algorithms of supervised learning use labeled data to get trained. The models take direct feedback to confirm whether the output that is being predicted is, indeed, correct. Moreover, both the input data and the output data are provided to the model, and the main aim here is to train the model to predict the output upon receiving new data. Supervised learning offers accurate results and can largely be divided into two parts, classification and regression.
Unsupervised learning: The algorithms of unsupervised learning use unlabeled data for training purposes. In unsupervised learning, the models identify hidden data trends and do not take any feedback. The unsupervised learning model is only provided with input data. Unsupervised learning’s main aim is to identify hidden patterns to extract information from unknown sets of data. It can also be classified into two parts, clustering and associations. Unfortunately, unsupervised learning offers results that are comparatively less accurate.
Question - 59 : - What is Support Vector Machine (SVM) in Machine Learning?
Answer - 59 : -
SVM is a Machine Learning algorithm that is majorly used for classification. It is used on top of the high dimensionality of the characteristic vector.
The following is the code for SVM classifier:
# Introducing required libraries
from sklearn import datasets
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
# Stacking the Iris dataset
iris = datasets.load_iris()
# A -> features and B -> label
A = iris.data
B = iris.target
# Breaking A and B into train and test data
A_train, A_test, B_train, B_test = train_test_split(A, B, random_state = 0)
# Training a linear SVM classifier
from sklearn.svm import SVC
svm_model_linear = SVC(kernel = 'linear', C = 1).fit(A_train, B_train)
svm_predictions = svm_model_linear.predict(A_test)
# Model accuracy for A_test
accuracy = svm_model_linear.score(A_test, B_test)
# Creating a confusion matrix
cm = confusion_matrix(B_test, svm_predictions)
Question - 60 : - What is Cross-validation in Machine Learning?
Answer - 60 : -
Cross-validation allows a system to increase the performance of the given Machine Learning algorithm, which is fed a number of sample data from the dataset. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set and the rest of the parts are kept as train sets. Cross-validation consists of the following techniques:
- Holdout method
- K-fold cross-validation
- Stratified k-fold cross-validation
- Leave p-out cross-validation