Questions and answers - MCQ with explanation on Computer Science subjects like System Architecture, Introduction to Management, Math For Computer Science, DBMS, C Programming, System Analysis and Design, Data Structure and Algorithm Analysis, OOP and Java, Client Server Application Development, Data Communication and Computer Networks, OS, MIS, Software Engineering, AI, Web Technology and … So, there is a high probability of misclassification of the minority label as compared to the majority label. It implies that the value of the actual class is no and the value of the predicted class is also no. This is due to the fact that the elements need to be reordered after insertion or deletion. can be applied. If the given argument is a compound data structure like a list then python creates another object of the same type (in this case, a new list) but for everything inside old list, only their reference is copied. Often it is not clear which basis functions are the best fit for a given task. She enjoys photography and football. Bayes’ Theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. This type of function may look familiar to you if you remember y = mx + b from high school. The most important features which one can tune in decision trees are: Ans. Popularity based recommendation, content-based recommendation, user-based collaborative filter, and item-based recommendation are the popular types of recommendation systems. This can be used to draw the tradeoff with OverFitting. 15. Random forest creates each tree independent of the others while gradient boosting develops one tree at a time. 1. This data is referred to as out of bag data. Hashing is a technique for identifying unique objects from a group of similar objects. Causality applies to situations where one action, say X, causes an outcome, say Y, whereas Correlation is just relating one action (X) to another action(Y) but X does not necessarily cause Y. This can be changed by making changes to classifier parameters. Addition and deletion of records is time consuming even though we get the element of interest immediately through random access. This kind of learning involves an agent that will interact with the environment to create actions and then discover errors or rewards of that action. The model complexity is reduced and it becomes better at predicting. Answer: Option B Although it depends on the problem you are solving, but some general advantages are following: Receiver operating characteristics (ROC curve): ROC curve illustrates the diagnostic ability of a binary classifier. Therefore, this prevents unnecessary duplicates and thus preserves the structure of the copied compound data structure. Therefore, Python provides us with another functionality called as deepcopy. The outcome will either be heads or tails. Bootstrap Aggregation or bagging is a method that is used to reduce the variance for algorithms having very high variance. Thus, in this case, c[0] is not equal to a, as internally their addresses are different. Machine learning is a broad field and there are no specific machine learning interview questions that are likely to be asked during a machine learning engineer job interview because the machine learning interview questions asked will focus on the open job position the employer is trying to fill. The likelihood values are used to compare different models, while the deviances (test, naive, and saturated) can be used to determine the predictive power and accuracy. Ans. This is the part of distortion of a statistical analysis which results from the method of collecting samples. Intuitively, we may consider that deepcopy() would follow the same paradigm, and the only difference would be that for each element we will recursively call deepcopy. This relation between Y and X, with a degree of the polynomial as 1 is called Linear Regression. We assume that there exists a hyperplane separating negative and positive examples. For example, if cancer is related to age, then, using Bayes’ theorem, a person’s age can be used to more accurately assess the probability that they have cancer than can be done without the knowledge of the person’s age.Chain rule for Bayesian probability can be used to predict the likelihood of the next word in the sentence. For example, how long a car battery would last, in months. Constructing a decision tree is all about finding the attribute that returns the highest information gain (i.e., the most homogeneous branches). We can copy a list to another just by calling the copy function. K-Means is Unsupervised Learning, where we don’t have any Labels present, in other words, no Target Variables and thus we try to cluster the data based upon their coordinates and try to establish the nature of the cluster based on the elements filtered for that cluster. stress concentration, The Random forests are a collection of trees which work on sampled data from the original dataset with the final prediction being a voted average of all trees. With KNN, we predict the label of the unidentified element based on its nearest neighbour and further extend this approach for solving classification/regression-based problems. Every machine learning problem tends to have its own particularities. Answer: Option B There are other techniques as well –Cluster-Based Over Sampling – In this case, the K-means clustering algorithm is independently applied to minority and majority class instances. When we have are given a string of a’s and b’s, we can immediately find out the first location of a character occurring. Neural Networks requires processors which are capable of parallel processing. A hyperparameter is a variable that is external to the model whose value cannot be estimated from the data. It can learn in every step online or offline. The three methods to deal with outliers are:Univariate method – looks for data points having extreme values on a single variableMultivariate method – looks for unusual combinations on all the variablesMinkowski error – reduces the contribution of potential outliers in the training process. In ridge, the penalty function is defined by the sum of the squares of the coefficients and for the Lasso, we penalize the sum of the absolute values of the coefficients. The most common way to get into a machine learning career is to acquire the necessary skills. Supervised learning: [Target is present]The machine learns using labelled data. 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