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Unit 1 One Marks Question of Machine Learning Technique

Q1. Define machine learning.

Sol. Machine learning is the science of making machine learn like a human and improve their learning by experience 

Q2. What are the different types of machine learning algorithm ?

Sol. There are following types of machine learning algorithm are :

1. Supervised machine learning algorithm

2. Unsupervised machine learning algorithm

3. Semi-supervised machine learning algorithm

4. Reinforcement machine learning algorithm

Q3. What are the applications of machine learning ?

Sol. Applications of machine learning are :

1. Image recognition

2. Speech recognition

3. Medical diagnosis

4. Statistical arbitrage

5. Learning association

Q4. What are the advantages of machine learning ?

Sol. Advantages of machine learning :

1. Easily identifies trends and patterns.

2. No human intervention is needed.

3. Continuous improvement.

4. Handling multi-dimensional and multi-variety data.

Q5. What are the disadvantages of machine learning ?

Sol. Disadvantages of machine learning :

1. Data acquisition

2. Time and resources

3. Interpretation of results

4. High error-susceptibility

Q6. What is the role of machine learning in human life ?

Sol. Role of machine learning in human life :

1. Learning

2. Reasoning

3. Problem solving

4. Language understanding

Q7. What are the components of machine learning system ?

Sol. Components of machine learning system are :

1. Sensing

2. Segmentation

3. Feature extraction

4. Classification

5. Post processing

Q8. What are the classes of problem in machine learning ?

Sol. Classes of problem in machine learning are :

1. Classification

2. Regression

3. Clustering

4. Rule extraction

Q9. What are the issues related with machine learning ?

Ans. Issues related with machine learning are :

1. Data quality

2. Transparency

3. Traceability

4. Reproduction of results

Q10. What is decision tree ?

Ans. A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs and utility.

Q11. Define supervised learning.

Ans. Supervised learning is also known as associative learning, in which the network is trained by providing it with input and matching output patterns.

Q12. Define unsupervised learning ?

Ans. Unsupervised learning is also known as self-organization, in which an output unit is trained to respond to clusters of pattern within the input.

Q13. Define well defined learning problem.

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.

Q14. What are the features of learning problems ?

Ans. Features of learning problems are :

1. The class of tasks (T).

2. The measure of performance to be improved (P).

3. The source of experience (E).

Q15. Define decision tree learning.

Ans. Decision tree learning is the predictive modeling approaches used in statistics, data mining and machine learning. It uses a decision tree to go from observations about an item to conclusions about the

item’s target values.

Q16. What are the types of decision tree ?

Ans. There are two types of decision tree :

1. Classification tree

2. Regression tree

Q17. Define classification tree and regression tree.

Ans. Classification tree : A classification tree is an algorithm where the target variable is fixed. This algorithm is used to identify the class within which a target variable would fall.

Regression tree : A regression tree is an algorithm where the target variable is not fixed and this algorithm is used to predict its value.

Q18. Name different decision tree algorithm.

Ans. Different decision tree algorithms are :

1. ID3

2. C4.5


Q19 What are issues related to decision tree

Ans. Issues related with decision tree are :

1. Missing data

2. Multi-valued attribute

3. Continuous and integer valued input attributes

4. Continuous-valued output attributes

Q 20. What are the attribute selection measures used in decision tree ?

Ans. Attribute selection measures used in decision tree are :

1. Entropy

2. Information gain

3. Gain ratio

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