Q. What do you understand by Machine learning?
Machine learning is the form of Artificial Intelligence that deals with system programming and automates data analysis to enable computers to learn and act through experiences without being explicitly programmed.
For example, Robots are coded in such a way that they can perform the tasks based on data they collect from sensors. They automatically learn programs from data and improve with experiences.
Q. Differentiate between inductive learning and deductive learning?
In inductive learning, the model learns by examples from a set of observed instances to draw a generalized conclusion. On the other side, in deductive learning, the model first applies the conclusion, and then the conclusion is drawn.
Inductive learning is the method of using observations to draw conclusions.
Deductive learning is the method of using conclusions to form observations.
For example, if we have to explain to a kid that playing with fire can cause burns. There are two ways we can explain this to a kid; we can show training examples of various fire accidents or images of burnt people and label them as “Hazardous”. In this case, a kid will understand with the help of examples and not play with the fire. It is the form of Inductive machine learning. The other way to teach the same thing is to let the kid play with the fire and wait to see what happens. If the kid gets a burn, it will teach the kid not to play with fire and avoid going near it. It is the form of deductive learning.
Q. What is the difference between Data Mining and Machine Learning?
Data mining can be described as the process in which the structured data tries to abstract knowledge or interesting unknown patterns. During this process, machine learning algorithms are used.
Machine learning represents the study, design, and development of the algorithms which provide the ability to the processors to learn without being explicitly programmed.
Q. What is the meaning of Overfitting in Machine learning?
Overfitting can be seen in machine learning when a statistical model describes random error or noise instead of the underlying relationship. Overfitting is usually observed when a model is excessively complex. It happens because of having too many parameters concerning the number of training data types. The model displays poor performance, which has been overfitted.
Q. Why overfitting occurs?
The possibility of overfitting occurs when the criteria used for training the model is not as per the criteria used to judge the efficiency of a model.
Q. What is the method to avoid overfitting?
Overfitting occurs when we have a small dataset, and a model is trying to learn from it. By using a large amount of data, overfitting can be avoided. But if we have a small database and are forced to build a model based on that, then we can use a technique known as cross-validation. In this method, a model is usually given a dataset of a known data on which training data set is run and dataset of unknown data against which the model is tested. The primary aim of cross-validation is to define a dataset to “test” the model in the training phase. If there is sufficient data, ‘Isotonic Regression’ is used to prevent overfitting.
Q. Differentiate supervised and unsupervised machine learning.
In supervised machine learning, the machine is trained using labeled data. Then a new dataset is given into the learning model so that the algorithm provides a positive outcome by analyzing the labeled data. For example, we first require to label the data which is necessary to train the model while performing classification.
In the unsupervised machine learning, the machine is not trained using labeled data and let the algorithms make the decisions without any corresponding output variables.
Q. How does Machine Learning differ from Deep Learning?
Machine learning is all about algorithms which are used to parse data, learn from that data, and then apply whatever they have learned to make informed decisions.
Deep learning is a part of machine learning, which is inspired by the structure of the human brain and is particularly useful in feature detection.
Q. How is KNN different from k-means?
KNN or K nearest neighbors is a supervised algorithm which is used for classification purpose. In KNN, a test sample is given as the class of the majority of its nearest neighbors. On the other side, K-means is an unsupervised algorithm which is mainly used for clustering. In k-means clustering, it needs a set of unlabeled points and a threshold only. The algorithm further takes unlabeled data and learns how to cluster it into groups by computing the mean of the distance between different unlabeled points.
Q. What do you understand by Reinforcement Learning technique?
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.
Q. What is the trade-off between bias and variance?
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.
Q. What according to you, is the standard approach to supervised learning?
In supervised learning, the standard approach is to split the set of example into the training set and the test.
Q. Describe ‘Training set’ and ‘training Test’.
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.
Q. What are the common ways to handle missing data in a dataset?
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.
Q. What do you understand by ILP?
ILP stands for Inductive Logic Programming. It is a part of machine learning which uses logic programming. It aims at searching patterns in data which can be used to build predictive models. In this process, the logic programs are assumed as a hypothesis.
Q. What are the necessary steps involved in Machine Learning Project?
There are several essential steps we must follow to achieve a good working model while doing a Machine Learning Project. Those steps may include parameter tuning, data preparation, data collection, training the model, model evaluation, and prediction, etc.
Q. Describe Precision and Recall?
Precision and Recall both are the measures which are used in the information retrieval domain to measure how good an information retrieval system reclaims the related data as requested by the user.
Precision can be said as a positive predictive value. It is the fraction of relevant instances among the received instances.
On the other side, recall is the fraction of relevant instances that have been retrieved over the total amount or relevant instances. The recall is also known as sensitivity.
Q. What do you understand by Decision Tree in Machine Learning?
Decision Trees can be defined as the Supervised Machine Learning, where the data is continuously split according to a certain parameter. It builds classification or regression models as similar as a tree structure, with datasets broken up into ever smaller subsets while developing the decision tree. The tree can be defined by two entities, namely decision nodes, and leaves. The leaves are the decisions or the outcomes, and the decision nodes are where the data is split. Decision trees can manage both categorical and numerical data.
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