Q1 : What are the different Algorithm techniques in Machine Learning?
A : The different types of techniques in Machine Learning are
a) Supervised Learning
b) Unsupervised Learning
c) Semi-supervised Learning
d) Reinforcement Learning
e) Transduction
f) Learning to Learn
Q2 : What is the difference between supervised and unsupervised machine learning?
A : A Supervised learning is a process where it requires training labeled data. When it comes to Unsupervised learning it doesn’t require data labeling.
Q3 : What is the difference between Machine learning and Data Mining?
A : Data mining is about working on unstructured data and then extract it to a level where the interesting and unknown patterns are identified.
Machine learning is a process or a study whether it closely relates to design, development of the algorithms that provide an ability to the machines to capacity to learn.
Q4 : What is Machine learning?
A : 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 programmed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.
Q5 : What is inductive machine learning?
A : 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.
Q6 : List down various approaches for machine learning?
A : The different approaches to Machine Learning are
a) Concept Vs Classification Learning
b) Symbolic Vs Statistical Learning
c) Inductive Vs Analytical Learning
Q7 : What is algorithm independent machine learning?
A : Machine learning in where mathematical foundations is independent of any particular classifier or learning algorithm is referred to as algorithm independent machine learning.
Q8 : What is Perceptron in Machine Learning?
A : In Machine Learning, Perceptron is an algorithm for supervised classification of the input into one of several possible non-binary outputs.
Q9: What is Model Selection in Machine Learning?
A : 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.
Q10: What is not Machine Learning?
A : a) Artificial Intelligence
b) Rule-based inference
Q11 : What is ‘Training set’ and ‘Test set’?
A : 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.
Q12 : What is the standard approach to supervised learning?
A : The standard approach to supervised learning is to split the set of example into the training set and the test.
Q13 : What is the function of ‘Supervised Learning’?
A : a) Classifications
b) Speech recognition
c) Regression
d) Predict time series
e) Annotate strings
Q14 : What are the three stages to build the hypotheses or model in machine learning?
A : a) Model building
b) Model testing
c) Applying the model
Q15 : What is the function of ‘Unsupervised Learning’?
A : a) Find clusters of the data
b) Find low-dimensional representations of the data
c) Find interesting directions in data
d) Interesting coordinates and correlations
e) Find novel observations/ database cleaning
Q16 : What is ‘Overfitting’ in Machine learning?
A : In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting 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 overfit.
Q17 : Why overfitting happens?
A : The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.
Q18 : How can you avoid overfitting?
A : By using a lot of data overfitting can be avoided, overfitting 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.
Q19 : What is Genetic Programming?
A : 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.
Q20 : Which method is frequently used to prevent overfitting?
A : When there is sufficient data ‘Isotonic Regression’ is used to prevent an overfitting issue.
Q21 : What are Bayesian Networks (BN)?
A : Bayesian Network is used to represent the graphical model for probability relationship among a set of variables.
Q22 : What are the advantages of Naive Bayes?
A : 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 the interactions between features.
Q23 : Why instance-based learning algorithm sometimes referred to as Lazy learning algorithm?
A : Instance based learning algorithm is also referred to as Lazy learning algorithm as they delay the induction or generalization process until classification is performed.
Q24 : What is ensemble learning?
A : 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.
Q25 : Why ensemble learning is used?
A : Ensemble learning is used to improve the classification, prediction, function approximation etc of a model.
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