Predictive Analytics Practice Questions With Answers

Univariate analysis is the simplest form of analyzing data. It doesn’t deal with causes or relationships and its major purpose is to describe; it takes data, summarizes that data and finds patterns in the data. Multivariate analysis, on the other hand, is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.

Predictive analytics is a branch of advanced analytics that uses both new and historical data to forecast future activity, behavior, and trends. It involves applying statistical analysis techniques, analytical queries, and automated machine learning algorithms to data sets to create predictive models that place a numerical value, or score, on the likelihood of a particular event happening.

Data cleaning is a critical step in predictive analytics. This process involves checking for errors or inconsistencies in the data and correcting or deleting them. Data cleaning can significantly impact the results of your predictive model, as the accuracy of the predictions depends heavily on the quality of the data used in the model. It ensures that the datasets used for analysis are accurate, correct, consistent, and usable.

Feature selection is a process where you automatically select those features in your data that contribute most to the prediction variable or output in which you are interested. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

The AUC-ROC curve is a performance measurement for the classification problems at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. The higher the AUC, the better the model is at predicting 0s as 0s and 1s as 1s.

The lift curve is a popular technique in direct marketing. It measures the effectiveness of a predictive model calculated as the ratio between the results obtained with and without the predictive model. Lift is a measure of the effectiveness of a predictive model calculated as the ratio between the results obtained with and without the predictive model.

Cross-validation is a powerful preventative measure against overfitting. The idea is clever: Use your initial training data to generate multiple mini train-test splits. Use these splits to tune your model. In standard k-fold cross-validation, we partition the data into k subsets, called folds. Then, we iteratively train the algorithm on k-1 folds while using the remaining fold as the test set.

Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While many people may know how to create models, interpreting the output can be a significant challenge. In predictive analytics, it’s often used to predict and forecast future events, and can also identify the variables that influence the outcome.

A confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm. It’s often used in supervised learning and it allows the visualization of the performance of an algorithm. Each row of the matrix represents the instances in a predicted class, while each column represents the instances in an actual class.

Overfitting and underfitting are the two biggest causes for poor performance of machine learning algorithms. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. Underfitting refers to a model that can neither model the training data nor generalize to new data.