A data sample with values that are considerably different than the rest of the other data samples in the dataset is called an/a _____________.
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Which of the following best describes a way to create and use a validation set to avoid overfitting? random sub-sampling k-fold cross-validation leave-one-out cross-validation All of these Get All Week Quiz Answer: Machine Learning with Big Data Coursera Quiz Answerra Quiz Answer Post Views: 147 Related posts: Which categories of machine learning techniques are supervised?…
Horizontal Conflict is when there is conflict between _____ while Vertical Conflict is when there is conflict between _____. Manufacturer and Retailer / Retailers Manufacturer and Retailer / Retailer and Customer Retailers / Manufacturer and Customer Retailers / Manufacturer and Retailer Get All Week Quiz Answer: Introduction to Marketing Coursera Quiz Answer Post Views: 144…
If you have a slow computer, what are some possible culprits that could be causing this? High CPU usage Lots of I/O activity High RAM usage Too many processes running A slow computer could be a sign of lots of things, but it’s always smart to first check the utilization of your resources. Get All…
What is the main difference between classification and regression? In classification, you’re predicting a number, and in regression, you’re predicting a category. In classification, you’re predicting a category, and in regression, you’re predicting a number. There is no difference since you’re predicting a numeric value from the input variables in both tasks. In classification, you’re…
Which of the following is NOT a data quality issue? Scaled data Missing values Duplicate data Inconsistent data Get All Week Quiz Answer: Machine Learning with Big Data Coursera Quiz Answerra Quiz Answer Post Views: 167 Related posts: What is NOT machine learning? What is the sequence of the steps in the machine learning process?…
How is the accuracy rate calculated? Add the number of true positives and the number of false negatives. Divide the number of true positives by the number of true negatives. Divide the number of correct predictions by the total number of predictions Subtract the number of correct predictions from the total number of predictions. Get…