Two marks Questions with Answers

Two Marks Questions with Answers

Q.1 Define learning.

Ans.: Learning is a phenomenon and process which has manifestations of various aspects. Learning process includes gaining of new symbolic knowledge and development of cognitive skills through instruction and practice. It is also discovery of new facts and theories through observation and experiment.

Q.2 Define machine learning.

Ans.: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

Q.3 What is an influence of information theory on machine learning?

Ans.: Information theory is measures of entropy and information content. Minimum description length approaches to learning. Optimal codes and their relationship to optimal training sequences for encoding a hypothesis.

Q.4 What is meant by target function of a learning program?

Ans.: Target function is a method for solving a problem that an AI algorithm parses its training data to find. Once an algorithm finds its target function, that function can be used to predict results. The function can then be used to find output data related to inputs for real problems where, unlike training sets, outputs are not included.

Q.5 Define useful perspective on machine learning.

Ans.: One useful perspective on machine learning is that it involves searching a very large space of possible hypotheses to determine one that best fits the observed data and any prior knowledge held by the learner.

Q.6 Describe the issues in machine learning?

Ans.: Issues of machine learning are as follows:

• What learning algorithms to be used?

• How much training data is sufficient?

• When and how prior knowledge can guide the learning process?

• What is the best strategy for choosing a next training experience?

• What is the best way to reduce the learning task to one or more function approximation problems?

• How can the learner automatically alter its representation to improve its learning ability?

Q.7 What is decision tree?

Ans.:

• Decision tree learning is a method for approximating discrete-valued target functions, in which the learned function is represented by a decision tree.

• A decision tree is a tree where each node represents a feature (attribute), each link(branch) represents a decision(rule) and each leaf represents an outcome (categorical or continues value).

• A decision tree or a classification tree is a tree in which each internal node is labeled with an input feature. The arcs coming from a node labeled with a feature are labeled with each of the possible values of the feature.

Q.8 What are the nodes of decision tree?

Ans.: A decision tree has two kinds of nodes

1. Each leaf node has a class label, determined by majority vote of training examples reaching that leaf.

2. Each internal node is a question on features. It branches out according to the answers.

• Decision tree learning is a method for approximating discrete-valued target functions. The learned function is represented by a decision tree

Q.9 Why tree pruning useful in decision tree induction?

Ans.: When a decision tree is built, many of the branches will reflect anomalies in the training data due to noise or outliers. Tree pruning methods address this problem of overfitting the data. Such methods typically use statistical measures to remove the least reliable branches.

Q.10 What is tree pruning?

Ans.: Tree pruning attempts to identify and remove such branches, with the goal of improving classification accuracy on unseen data

Q.11 What is RULE POST-PRUNING?

Ans.:

• It is method for finding high accuracy hypotheses.

• Rule post-pruning involves the following steps:

1. Infer decision tree from training set

2. Convert tree to rules - one rule per branch

3. Prune each rule by removing preconditions that result in improved estimatedaccuracy

4. Sort the pruned rules by their estimated accuracy and consider them in this sequence when classifying unseen instances

Q.12 Why convert the decision tree to rules before pruning?

Ans.:

• Converting to rules allows distinguishing among the different contexts in which a decision node is used.

• Converting to rules removes the distinction between attribute tests that occur near the root of the tree and those that occur near the leaves.

• Converting to rules improves readability. Rules are often easier for to if understand

Q.13 What do you mean by least square method?

Ans.: Least squares is a statistical method used to determine a line of best fit by minimizing the sum of squares created by a mathematical function. A "square" is determined by squaring the distance between a data point and the regression line or mean value of the data set

Q.14 What is linear discriminant function?

Ans.: LDA is a supervised learning algorithm, which means that it requires a labelled training set of data points in order to learn the Linear Discriminant function.

Q.15 What is a support vector in SVM?

Ans.: Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier.

Q.16 What is support vector machines?

Ans.: A Support Vector Machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.

Q.17 Define logistic regression.

Ans.: Logistic regression is supervised learning technique. It is used for predicting the categorical dependent variable using a given set of independent variables.

Q.18 List out types of machine learning.

Ans.: Types of machine learnings are supervised, semi-supervised, unsupervised and Reinforcement Learning.

Q.19 What is random forest?

Ans.: Random forest is an ensemble learning technique that combines multiple decision trees, implementing the bagging method and results in a robust model with low variance.

Q.20 What are the five popular algorithms of machine learning?

Ans.: Popular algorithms are Decision Trees, Neural Networks (back propagation), Probabilistic networks, Nearest Neighbor and Support vector machines.

Q.21 What is the function of 'Supervised Learning'?

Ans.: Function of 'Supervised Learning' are Classifications, Speech recognition, Regression, Predict time series and Annotate strings.

Q.22 What are the advantages of Naive Bayes?

Ans.: 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.

Q.23 What is regression?

Ans.: Regression is a method to determine the statistical relationship between a dependent variable and one or more independent variables.

Q.24 Explain linear and non-linear regression model.

Ans.: In linear regression models, the dependence of the response on the regressors is defined by a linear function, which makes their statistical analysis mathematically tractable. On the other hand, in nonlinear regression models, this dependence is defined by a nonlinear function, hence the mathematical difficulty in their analysis.

Q.25 What is regression analysis used for?

Ans.: Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables.

Q.26 List two properties of logistic regression.

Ans.:

1. The dependent variable in logistic regression follows Bernoulli Distribution. 2. Estimation is done through maximum likelihood.

Q.27 What is the goal of logistic regression?

Ans.: The goal of logistic regression is to correctly predict the category of outcome for individual cases using the most parsimonious model. To accomplish this goal, a model is created that includes all predictor variables that are useful in predicting the response variable.

Q.28 Define supervised learning.

Ans.: Supervised learning in which the network is trained by providing it with input and matching output patterns. These input-output pairs are usually provided by an external teacher.