Matplotlib can be used for?
Anonymous Quiz
3%
Web Development
87%
Data Visualization
6%
Data Extraction
4%
None of the above
๐1
Which of the following is not used for Machine Learning/Deep Learning?
Anonymous Quiz
5%
Scikit-learn
6%
Tensorflow
8%
Keras
81%
JavaScript
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 โcentroidsโ, 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 ๐๐
[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 โcentroidsโ, 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 ๐๐
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 humanโs 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.
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 humanโs 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.
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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 โcurse of dimensionalityโ?
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 ๐๐
[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 โcurse of dimensionalityโ?
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 ๐๐
Which of the following maybe involved in the data science project?
Anonymous Quiz
3%
Data Cleaning
4%
Data Visualization
2%
Feature selection
3%
Exploratory data analysis
89%
All of the above
Which of the following is not a machine learning algorithm?
Anonymous Quiz
2%
Linear Regression
6%
K-means clustering
87%
Data Cleaning
5%
Logistic Regression
DATA SCIENCE INTERVIEW QUESTIONS
[ PART - 13]
๐1. ๐๐จ๐ฐ ๐ญ๐จ ๐ข๐๐๐ง๐ญ๐ข๐๐ฒ ๐ ๐๐๐ฎ๐ฌ๐ ๐ฏ๐ฌ. ๐ ๐๐จ๐ซ๐ซ๐๐ฅ๐๐ญ๐ข๐จ๐ง? ๐๐ข๐ฏ๐ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐๐ฌ.
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.
๐2. ๐ฉ๐ซ๐๐๐ข๐ฌ๐ข๐จ๐ง, ๐๐๐๐ฎ๐ซ๐๐๐ฒ ๐๐ง๐ ๐ซ๐๐๐๐ฅ๐ฅ?
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.
๐3. ๐๐ก๐จ๐จ๐ฌ๐ ๐ค ๐ข๐ง ๐ค-๐ฆ๐๐๐ง๐ฌ?
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.
๐4. ๐ฐ๐จ๐ซ๐2๐ฏ๐๐ ๐ฆ๐๐ญ๐ก๐จ๐๐ฌ?
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.
๐5. P๐ซ๐ฎ๐ง๐ข๐ง๐ ๐ข๐ง ๐๐๐ฌ๐ ๐จ๐ ๐๐๐๐ข๐ฌ๐ข๐จ๐ง ๐ญ๐ซ๐๐๐ฌ?
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 ๐๐
[ PART - 13]
๐1. ๐๐จ๐ฐ ๐ญ๐จ ๐ข๐๐๐ง๐ญ๐ข๐๐ฒ ๐ ๐๐๐ฎ๐ฌ๐ ๐ฏ๐ฌ. ๐ ๐๐จ๐ซ๐ซ๐๐ฅ๐๐ญ๐ข๐จ๐ง? ๐๐ข๐ฏ๐ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐๐ฌ.
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.
๐2. ๐ฉ๐ซ๐๐๐ข๐ฌ๐ข๐จ๐ง, ๐๐๐๐ฎ๐ซ๐๐๐ฒ ๐๐ง๐ ๐ซ๐๐๐๐ฅ๐ฅ?
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.
๐3. ๐๐ก๐จ๐จ๐ฌ๐ ๐ค ๐ข๐ง ๐ค-๐ฆ๐๐๐ง๐ฌ?
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.
๐4. ๐ฐ๐จ๐ซ๐2๐ฏ๐๐ ๐ฆ๐๐ญ๐ก๐จ๐๐ฌ?
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.
๐5. P๐ซ๐ฎ๐ง๐ข๐ง๐ ๐ข๐ง ๐๐๐ฌ๐ ๐จ๐ ๐๐๐๐ข๐ฌ๐ข๐จ๐ง ๐ญ๐ซ๐๐๐ฌ?
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 ๐๐
๐3
Which of the following is used to read csv file in python using pandas?
import pandas as pd
import pandas as pd
Anonymous Quiz
10%
pd.readcsv(file.csv)
80%
pd.read_csv("file.csv")
6%
pd.read(file)
4%
pd(read_csv.file)
๐๐จ๐๐๐ฒ'๐ฌ ๐ข๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐ฎ๐๐ฌ๐ญ ๐ ๐๐ง๐ฌ
DATA SCIENCE INTERVIEW QUESTIONS
[PART - 14]
๐1. ๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐ฌ๐๐ฅ๐๐๐ญ๐ข๐จ๐ง ๐ฆ๐๐ญ๐ก๐จ๐๐ฌ ๐๐จ๐ซ ๐ฌ๐๐ฅ๐๐๐ญ๐ข๐ง๐ ๐ญ๐ก๐ ๐ซ๐ข๐ ๐ก๐ญ ๐ฏ๐๐ซ๐ข๐๐๐ฅ๐๐ฌ ๐๐จ๐ซ ๐๐ฎ๐ข๐ฅ๐๐ข๐ง๐ ๐๐๐๐ข๐๐ข๐๐ง๐ญ ๐ฉ๐ซ๐๐๐ข๐๐ญ๐ข๐ฏ๐ ๐ฆ๐จ๐๐๐ฅ๐ฌ?
Ans. Some of the Feature selection techniques are: Information Gain, Chi-square test, Correlation Coefficient, Mean Absolute Difference (MAD), Exhaustive selection, Forward selection, Regularization.
๐2. ๐๐ซ๐๐๐ญ ๐ฆ๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐ฏ๐๐ฅ๐ฎ๐๐ฌ?
Ans. They are:
1. List wise or case deletion
2. Pairwise deletion
3. Mean substitution
4. Regression imputation
5. Maximum likelihood.
๐3. ๐๐ฌ๐ฌ๐ฎ๐ฆ๐ฉ๐ญ๐ข๐จ๐ง๐ฌ ๐ฎ๐ฌ๐๐ ๐ข๐ง ๐ฅ๐ข๐ง๐๐๐ซ ๐ซ๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง? ๐๐ก๐๐ญ ๐ฐ๐จ๐ฎ๐ฅ๐ ๐ก๐๐ฉ๐ฉ๐๐ง ๐ข๐ ๐ญ๐ก๐๐ฒ ๐๐ซ๐ ๐ฏ๐ข๐จ๐ฅ๐๐ญ๐๐?
Ans. 1. Linear relationship.
2. Multivariate normality.
3. no or little multicollinearity.
4. no auto-correlation.
5. Homoscedasticity.
Data to be analyzed by linear regression were sampled violate one or more of the linear regression assumptions, the results of the analysis may be incorrect or misleading.
๐4. ๐๐จ๐ฐ ๐ข๐ฌ ๐ญ๐ก๐ ๐ ๐ซ๐ข๐ ๐ฌ๐๐๐ซ๐๐ก ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ ๐๐ข๐๐๐๐ซ๐๐ง๐ญ ๐๐ซ๐จ๐ฆ ๐ญ๐ก๐ ๐ซ๐๐ง๐๐จ๐ฆ ๐ฌ๐๐๐ซ๐๐ก ๐ญ๐ฎ๐ง๐ข๐ง๐ ๐ฌ๐ญ๐ซ๐๐ญ๐๐ ๐ฒ?
Ans. Random search differs from grid search in that we no longer provide an explicit set of possible values for each hyperparameter; rather, we provide a statistical distribution for each hyperparameter from which values are sampled. Essentially, we define a sampling distribution for each hyperparameter to carry out a randomized search.
๐5. ๐๐ฌ ๐ข๐ญ ๐ ๐จ๐จ๐ ๐ญ๐จ ๐๐จ ๐๐ข๐ฆ๐๐ง๐ฌ๐ข๐จ๐ง๐๐ฅ๐ข๐ญ๐ฒ ๐ซ๐๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐๐๐๐จ๐ซ๐ ๐๐ข๐ญ๐ญ๐ข๐ง๐ ๐ ๐๐ฎ๐ฉ๐ฉ๐จ๐ซ๐ญ ๐๐๐๐ญ๐จ๐ซ ๐๐จ๐๐๐ฅ?
๐ns. Support Vector Machine Learning Algorithm performs better in the reduced space. It is beneficial to perform dimensionality reduction before fitting an SVM if the number of features is large when compared to the number of observations.
๐6. ๐๐๐ ๐๐ฎ๐ซ๐ฏ๐?
Ans ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds.
ENJOY LEARNING ๐๐
DATA SCIENCE INTERVIEW QUESTIONS
[PART - 14]
๐1. ๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐ฌ๐๐ฅ๐๐๐ญ๐ข๐จ๐ง ๐ฆ๐๐ญ๐ก๐จ๐๐ฌ ๐๐จ๐ซ ๐ฌ๐๐ฅ๐๐๐ญ๐ข๐ง๐ ๐ญ๐ก๐ ๐ซ๐ข๐ ๐ก๐ญ ๐ฏ๐๐ซ๐ข๐๐๐ฅ๐๐ฌ ๐๐จ๐ซ ๐๐ฎ๐ข๐ฅ๐๐ข๐ง๐ ๐๐๐๐ข๐๐ข๐๐ง๐ญ ๐ฉ๐ซ๐๐๐ข๐๐ญ๐ข๐ฏ๐ ๐ฆ๐จ๐๐๐ฅ๐ฌ?
Ans. Some of the Feature selection techniques are: Information Gain, Chi-square test, Correlation Coefficient, Mean Absolute Difference (MAD), Exhaustive selection, Forward selection, Regularization.
๐2. ๐๐ซ๐๐๐ญ ๐ฆ๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐ฏ๐๐ฅ๐ฎ๐๐ฌ?
Ans. They are:
1. List wise or case deletion
2. Pairwise deletion
3. Mean substitution
4. Regression imputation
5. Maximum likelihood.
๐3. ๐๐ฌ๐ฌ๐ฎ๐ฆ๐ฉ๐ญ๐ข๐จ๐ง๐ฌ ๐ฎ๐ฌ๐๐ ๐ข๐ง ๐ฅ๐ข๐ง๐๐๐ซ ๐ซ๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง? ๐๐ก๐๐ญ ๐ฐ๐จ๐ฎ๐ฅ๐ ๐ก๐๐ฉ๐ฉ๐๐ง ๐ข๐ ๐ญ๐ก๐๐ฒ ๐๐ซ๐ ๐ฏ๐ข๐จ๐ฅ๐๐ญ๐๐?
Ans. 1. Linear relationship.
2. Multivariate normality.
3. no or little multicollinearity.
4. no auto-correlation.
5. Homoscedasticity.
Data to be analyzed by linear regression were sampled violate one or more of the linear regression assumptions, the results of the analysis may be incorrect or misleading.
๐4. ๐๐จ๐ฐ ๐ข๐ฌ ๐ญ๐ก๐ ๐ ๐ซ๐ข๐ ๐ฌ๐๐๐ซ๐๐ก ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ ๐๐ข๐๐๐๐ซ๐๐ง๐ญ ๐๐ซ๐จ๐ฆ ๐ญ๐ก๐ ๐ซ๐๐ง๐๐จ๐ฆ ๐ฌ๐๐๐ซ๐๐ก ๐ญ๐ฎ๐ง๐ข๐ง๐ ๐ฌ๐ญ๐ซ๐๐ญ๐๐ ๐ฒ?
Ans. Random search differs from grid search in that we no longer provide an explicit set of possible values for each hyperparameter; rather, we provide a statistical distribution for each hyperparameter from which values are sampled. Essentially, we define a sampling distribution for each hyperparameter to carry out a randomized search.
๐5. ๐๐ฌ ๐ข๐ญ ๐ ๐จ๐จ๐ ๐ญ๐จ ๐๐จ ๐๐ข๐ฆ๐๐ง๐ฌ๐ข๐จ๐ง๐๐ฅ๐ข๐ญ๐ฒ ๐ซ๐๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐๐๐๐จ๐ซ๐ ๐๐ข๐ญ๐ญ๐ข๐ง๐ ๐ ๐๐ฎ๐ฉ๐ฉ๐จ๐ซ๐ญ ๐๐๐๐ญ๐จ๐ซ ๐๐จ๐๐๐ฅ?
๐ns. Support Vector Machine Learning Algorithm performs better in the reduced space. It is beneficial to perform dimensionality reduction before fitting an SVM if the number of features is large when compared to the number of observations.
๐6. ๐๐๐ ๐๐ฎ๐ซ๐ฏ๐?
Ans ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds.
ENJOY LEARNING ๐๐
โค1๐1