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Predictive Analytics In Human Resource Management A Hands-on Approach 1st Edition Shivinder Nijjer, Sahil Raj - Solutions
List the steps used to define a variable as factor variable for implementation of KNN.
What is the purpose of trainControl function in R?
How can a user change the optimality criteria used for the selection of model using train function?
List the benefits of using caret package in R.
Discuss the ways in which KNN model can be built in R.
Explain how a predictive model can be used to predict turnover in organisations.
Discuss the role of job attitudes in explaining turnover intention.
Discuss the role of PO fit in explaining turnover intention.
Define employer choice strategy.
What is meant by resource-based view of the firm?
List some facts and figures depicting how turnover is a problem for IT firms.
Define turnover intent.
List the causes of separation in an organisation.
List the types of turnover.
Define turnover.
What do you understand to be the meaning of turnover?
data points on age, gender, academic grades, and performance ranking from employees working at the same job position at the same level in similar organisations. Now build a neural network to predict performance ranking based on three covariates – age, gender, and grades. Normalise the dataset and
Collect a small sample of about
Using the code for min-max normalisation, write a code for z-normalisation and test it see if results are different.
Why is it becoming all the more relevant now to generate individualised career growth recommendations for employees?
How can predictive analytics be used to identify and address the skill gaps in hiring and supply of employees in a firm?
How can predictive analytics improve the speed of hiring?
How can predictive analytics be used to assess the effectiveness and efficiency of the hiring process?
How can predictive analytics be used to gauge a competitor’s job offerings?
How can predictive analytics improve the quality of the hiring process?
List the six steps implemented in organisations to generate a comprehensive supply chain analysis using predictive analytics.
Explain the environmental dynamicity which has led to changes in recruitment practices.
List some probable future aspects emerging from the application of the model.
Briefly explain how to generate recommendations from the model results.
List the steps used to build confusion matrix and generate misclassification error for checking the accuracy of the model.
What is the role of the compute function in validating the outcomes of neuralnet?
Discuss the components of the output of a model built using neuralnet package.
Explain how Sample function works in R.
What is the Pareto Rule? How is splitting as per the Pareto rule implemented in R?
Discuss the need to split data into test and training data.
Explain min-max normalisation and z-normalisation.
Discuss the characteristics of neuralnet package in R.
List the data sources which can be used to build a predictive model for selection.
Explain the application of system view/process approach to the problem of predictive selection.
Justify how classification is an appropriate prediction technique for the problem of selection, as discussed in the text.
Summarise the application of a holistic approach to the problem of predictive selection in your terms.
List a few challenges selection process has to face in the current business environment.
Detail the limitations of following a traditional selection process.
List the steps followed in the traditional selection process.
Explain the meaning of the ‘changing nature of HR practices’.
Why it has become important for the HR department to quantify its performance?
What is the purpose of recruitment and selection?
From the confusion matrix created in Problem 1, determine the misclassification error and the five metrics used to evaluate the model performance, derived from confusion matrix. Comment on the usability of the model for classification in the future.
as NonPerforming (81 correct classifications). Create a confusion matrix for the described scenario.
were correct), and
as HighPerforming (out of which
For a business data classification, the algorithm was able to classify
List different basis used for the choice of an analytical tool and platform.
Discuss different measures used for validating KNN.
Define Gini index.
What is the relation between ROC and AUC?
What is the benefit of using ROC curve?
What is understood by ROC curve?
List the purposes that the lift chart serves.
Write the code in R to compute misclassification error.
Define misclassification error.
Which R package has inbuilt confusion matrix function? What are the outcomes of this function?
Write the code for manually building a confusion matrix in R.
What is NoInformation rate?
List and explain the five metrics derived from the confusion matrix.
Define confusion matrix.
What is the purpose of corrected Rand index?
Define external validation of cluster.
List the two most widely used clustering indices and the functions used to implement them in R.
Define connectivity.
Define separation.
Define compactness.
List the three clustering indices and their purpose.
What is the basic objective of K-means clustering?
Write code for implementing cross-validation in R, with and without repeats.
Explain in simple terms how cross-validation of data works.
What is k-fold cross-validation?
List the purpose of cross-validation of data.
Discuss the two ways in which data can split in R.
Why is overfitting a big problem in data analytics?
Discuss the need to split the dataset into training and test data.
Define overfitting.
What is Pareto’s rule?
Discuss the three reasons explaining the need for validating the analytical outcomes.
Given a function with the coordinates {(2,3), (3,4), (6,7), (10,3)}, find Euclidean distance, Minkowski distance, and Manhattan distance for the dataset.
Search the Internet to find commercial software specialising in HR analytics/people analytics/workforce analytics. Also, attempt to find their pricing schemes. Tabulate and report your findings.
List the five tools in the Tableau suite.
What are the two key reasons for the success of Tableau?
What is one major advantage of using PowerBI?
List some differences between R and Python.
Explain a typical R interface.
Discuss some reasons for the popularity of R.
List some popular analytical tools.
List some common applications of text analytics and their role in HRM.
List and explain the key steps involved in text analytics.
Define text analytics.
Explain an application of K-means clustering.
Discuss the limitations of K-means clustering.
List the two ways in iterations can be stopped in K-means clustering.
Explain the basic functioning of K-means clustering.
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