Data Science & Machine Learning

DATA SCIENCE INTERVIEW QUESTIONS [ PART - 13] 𝐐1. 𝐇𝐨𝐰 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐚 𝐜𝐚𝐮𝐬𝐞 𝐯𝐬. 𝐚 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧? 𝐆𝐢𝐯𝐞 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬. Ans. While causation and correlation can exist at the same time, correlation does not imply causation. Causation explicitly applies to cases where action A causes outcome B. On the other hand, correlation is simply a relationship. Correlation between Ice cream sales and sunglasses sold. As the sales of ice creams is increasing so do the sales of sunglasses. Causation takes a step further than correlation. 𝐐2. 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧, 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐚𝐧𝐝 𝐫𝐞𝐜𝐚𝐥𝐥? Ans. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. The precision is the proportion of relevant results in the list of all returned search results. Accuracy is the measurement used to determine which model is best at identifying relationships and patterns between variables in a dataset based on the input, or training, data. 𝐐3. 𝐜𝐡𝐨𝐨𝐬𝐞 𝐤 𝐢𝐧 𝐤-𝐦𝐞𝐚𝐧𝐬? Ans. There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster. 𝐐4. 𝐰𝐨𝐫𝐝2𝐯𝐞𝐜 𝐦𝐞𝐭𝐡𝐨𝐝𝐬? Ans. Word2vec is a technique for natural language processing published in 2013. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. 𝐐5. P𝐫𝐮𝐧𝐢𝐧𝐠 𝐢𝐧 𝐜𝐚𝐬𝐞 𝐨𝐟 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐭𝐫𝐞𝐞𝐬? Ans. Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. ENJOY LEARNING 👍👍

DATA SCIENCE INTERVIEW QUESTIONS [PART -12] Q. What are Entropy and Information gain in Decision tree algorithm? A. Entropy is a measure of impurity or uncertainty in a set of data used in information theory. It determines how data is split by a decision tree. The quantity of information improved in the nodes before splitting them for making subsequent judgments can be characterized as the information obtained in the decision tree. Q. What Will Happen If the Learning Rate Is Set inaccurately (Too Low or Too High)? A. A high learning rate in gradient descent will cause the learning to jump over global minima, whereas a low learning rate will cause the learning to take too long to converge or become stuck in an unwanted local minimum. Q. What is meant by ‘curse of dimensionality’? A. The problem produced by the exponential rise in volume associated with adding extra dimensions to Euclidean space is known as the "curse of dimensionality." The curse of dimensionality states that as the number of characteristics grows, the error grows as well. It refers to the fact that high-dimensional algorithms are more difficult to build and often have a running duration that is proportional to the dimensions. A higher number of dimensions theoretically allows for more information to be stored, but in practice, it rarely helps because real-world data contains more noise and redundancy. Q. Difference between remove, del and pop? A. remove function removes the first matching value/object. It does not do anything with the indexing. del function removes the item at a specific index. And pop removes the item at a specific index and returns it. ENJOY LEARNING 👍👍

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Today's Question -  What are some ways I can make my model more robust to outliers? There are several ways to make a model more robust to outliers, from different points of view (data preparation or model building). An outlier in the question and answer is assumed being unwanted, unexpected, or a must-be-wrong value to the human’s knowledge so far (e.g. no one is 200 years old) rather than a rare event which is possible but rare. Outliers are usually defined in relation to the distribution. Thus outliers could be removed in the pre-processing step (before any learning step), by using standard deviations (Mean +/- 2*SD), it can be used for normality. Or interquartile ranges Q1 - Q3, Q1 - is the "middle" value in the first half of the rank-ordered data set, Q3 - is the "middle" value in the second half of the rank-ordered data set. It can be used for not normal/unknown as threshold levels. Moreover, data transformation (e.g. log transformation) may help if data have a noticeable tail. When outliers related to the sensitivity of the collecting instrument which may not precisely record small values, Winsorization may be useful. This type of transformation has the same effect as clipping signals (i.e. replaces extreme data values with less extreme values). Another option to reduce the influence of outliers is using mean absolute difference rather mean squared error. For model building, some models are resistant to outliers (e.g. tree-based approaches) or non-parametric tests. Similar to the median effect, tree models divide each node into two in each split. Thus, at each split, all data points in a bucket could be equally treated regardless of extreme values they may have.

Data Science Interview Questions [Part - 11] Q1.  Difference between R square and Adjusted R Square. Ans. One main difference between R2 and the adjusted R2: R2 assumes that every single variable explains the variation in the dependent variable. The adjusted R2 tells you the percentage of variation explained by only the independent variables that actually affect the dependent variable. Q2. Difference between Precision and Recall. Ans.  When it comes to precision we're talking about the true positives over the true positives plus the false positives. As opposed to recall which is the number of true positives over the true positives and the false negatives. Q3.  Assumptions of Linear Regression. Ans.  There are four assumptions associated with a linear regression model: Linearity: The relationship between X and the mean of Y is linear. Homoscedasticity: The variance of residual is the same for any value of X. Independence: Observations are independent of each other. The fourth one is normality. Q4. Difference between Random Forest and Decision Tree. Ans. A decision tree combines some decisions, whereas a random forest combines several decision trees. Thus, it is a long process, yet slow. Whereas, a decision tree is fast and operates easily on large data sets, especially the linear one. The random forest model needs rigorous training. Q5. How does K-means work? Ans.  K-means clustering uses “centroids”, K different randomly-initiated points in the data, and assigns every data point to the nearest centroid. After every point has been assigned, the centroid is moved to the average of all of the points assigned to it. Q6.  How do you generally choose among different classification models to decide which one is performing the best? Ans. Here are some important considerations while choosing an algorithm: Size of the training data, Accuracy and/or Interpretability of the output, Speed or Training time, Linearity and number of features. Q7. How do you perform feature selection? Ans. Unsupervised: Do not use the target variable (e.g. remove redundant variables). Correlation.  Supervised: Use the target variable (e.g. remove irrelevant variables). Wrapper: Search for well-performing subsets of features. RFE. Q8. What is an intercept in a Linear Regression? What is its significance? Ans. The intercept (often labeled as constant) is the point where the function crosses the y-axis. In some analysis, the regression model only becomes significant when we remove the intercept, and the regression line reduces to Y = b*X + error.  The intercept (often labeled the constant) is the expected mean value of Y when all X="0. Start with a regression equation with one predictor, X. If X sometimes equals 0, the intercept is simply the expected mean value of Y at that value. If X never equals 0, then the intercept has no intrinsic meaning. ENJOY LEARNING 👍👍