Predictive Modeling Fundamentals I Cognitive Class Exam Answers

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Introduction to Predictive Modeling Fundamentals I

Predictive modeling is a powerful technique used in data science and machine learning to predict outcomes based on data. It involves building a model from historical data with known outcomes (the training data) and using this model to make predictions on new data where the outcomes are unknown.

Key Concepts:

1. Predictive Modeling: The process of creating a model that predicts the value of a target variable based on input features.
2. Target Variable: Also known as the dependent variable or response variable, this is the variable you are trying to predict.
3. Features: These are the input variables used to predict the target variable. They can be numerical, categorical, or even text data.
4. Training Data: Historical data used to train or build the predictive model. It consists of features and their corresponding known target variable values.
5. Model Building: The process of selecting an appropriate algorithm, training the model on the training data, and optimizing its parameters to achieve accurate predictions.
6. Prediction: Applying the trained model to new data to predict the target variable’s value.

Steps in Predictive Modeling:

1. Data Collection: Gather relevant data that includes both features and the target variable.
2. Data Cleaning: Preprocess the data to handle missing values, outliers, and format the data appropriately for modeling.
3. Feature Selection/Engineering: Choose relevant features that are likely to have a strong influence on predicting the target variable. This might involve transforming variables or creating new features.
4. Model Selection: Select an appropriate algorithm based on the nature of the problem (e.g., regression for continuous target variables, classification for categorical target variables).
5. Model Training: Use the training data to fit the model to learn the relationship between features and the target variable.
6. Model Evaluation: Assess the model’s performance using evaluation metrics such as accuracy, precision, recall, or mean squared error, depending on the problem type.
7. Model Tuning: Fine-tune the model by adjusting hyperparameters or trying different algorithms to improve its performance.
8. Prediction: Deploy the trained model to make predictions on new data where the target variable is unknown.

Common Algorithms Used:

• Linear Regression: For predicting continuous variables.
• Logistic Regression: For predicting categorical variables.
• Decision Trees: For both regression and classification tasks.
• Random Forests: Ensemble method based on decision trees.
• Gradient Boosting Machines (GBM): Another ensemble method that builds trees sequentially.

Applications:

• Finance: Predicting stock prices, credit risk assessment.
• Healthcare: Diagnosing diseases based on symptoms.
• Marketing: Targeted advertising and customer segmentation.
• E-commerce: Recommender systems and predicting customer behavior.

Challenges:

• Overfitting: When a model performs well on training data but fails to generalize to new, unseen data.
• Underfitting: When a model is too simplistic to capture the underlying patterns in the data.
• Data Quality: Poor quality data can lead to inaccurate predictions.
• Interpretability: Complex models like neural networks might be accurate but difficult to interpret.

Conclusion:

Predictive modeling is a crucial tool in extracting insights and making informed decisions from data. By understanding the fundamentals of predictive modeling, you can leverage data effectively to solve real-world problems across various domains.

Predictive Modeling Fundamentals I Cognitive Class Certification Answers

Module 1: Introduction to Data Mining Quiz Answers

Question 1: Which of the following applications would require the use of data mining? Select all that apply.

• Predicting the outcome of flipping a fair coin
• Determining which products in a store are likely to be purchased together
• Predicting future stock prices using historical records
• Determining the total number of products sold by a store
• Sorting a student database by gender

Question 2: Which of the following is NOT a section of the Modeler Interface?

• Nodes
• Palettes
• Stream Canvas
• Stream, Outputs, and Model Manager
• All of the above are sections of the Modeler Interface

Question 3: Which of the following is NOT a part of the Cross-Industry Process for Data Mining?

• Business Understanding
• Evaluation
• Data Preparation
• Data Storage
• Modeling

Module 2: The Data Mining Process Quiz Answers

Question 1: Which phase of the data mining process focuses on understanding the project requirements and objectives?

• Data Understanding
• Data Exploration
• Data Preprocessing
• Business Understanding
• Data Preparation

Question 2: Which Data Preprocessing task focuses on removing outliers and filling in missing values?

• Data Integration
• Data Cleaning
• Data Transformation
• Data Reduction
• None of the above

Question 3: The IBM SPSS Modeler supports which data type?

• Nominal
• Categorical
• Ordinal
• Continuous
• All of the above

Module 3: Modeling Techniques Quiz Answers

Question 1: Which of the following methods are commonly used for supervised learning tasks? Select all that apply.

• Neural Networks
• Decision Trees
• K-Means
• CARMA
• Regression

Question 2: Classification is a subset of supervised learning that focuses on modeling continuous variables. True or false?

• True
• False

Question 3: Which of the following algorithms is NOT supported by the SPSS Modeler?

• Logistic Regression
• CARMA
• K-Means
• Apriori
• All of the above algorithms are supported

Module 4: Model Evaluation Quiz Answers

Question 1: What is the term for a negative data point that is incorrectly classified as positive?

• True Negative
• False Positive
• True Positive
• False Negative
• None of the above

Question 2: Which of the following is NOT a cost-sensitive performance metric?

• Precision
• Accuracy
• Specificity
• Sensitivity
• All of the above metrics are cost-sensitive

Question 3: What is the formula for the precision metric?

• (False Positive) / (True Negative + True Positive)
• (True Positive) / (True Positive + False Positive)
• (False Positive) / (True Positive + False Positive)
• (True Positive) / (True Positive + False Negative)
• (True Negative) / (True Negative + False Positive)

Module 5: Deployment on IBM Bluemix Quiz Answers

Question 1: In general, the testing dataset should be significantly larger than the training dataset. True or false?

• True
• False

Question 2: Which of the following is NOT a model deployment solution?

• Bluemix
• CRISP-DM
• IBM Collaboration and Deployment Services
• SPSS Solution Publisher
• All of the above are model deployment solutions

Question 3: Which of the following statements are true of IBM Bluemix? Select all that apply.

• Bluemix generally takes about a week to deploy an app
• Bluemix is supported by a growing community
• Bluemix is closed-source
• Bluemix provides a self-service application-hosting environment
• Bluemix provides built-in load-balancing capabilities

Predictive Modeling Fundamentals I Final Exam Answers

Question 1: Which of the following suggests that the model is overfitting the data?

• High accuracy on training data and high accuracy on testing data
• Low accuracy on training data and high accuracy on testing data
• Low accuracy on training data and low accuracy on testing data
• High accuracy on training data and low accuracy on testing data
• None of the above

Question 2: Which of the following tasks would require the use of data mining?

• Predicting the outcome of rolling two fair dice
• Determining which products in a store are likely to be purchased together
• Sorting a customer database by age
• Computing the number of products sold over a given time period
• All of the above

Question 3: Suppose you have collected data on your customers and you wish to determine the demographics they fall into. Which technique is best suited for this task?

• Neural Network
• Logistic Regression
• Clustering
• Linear Regression
• Decision Tree

Question 4: Suppose you wish to use data mining in order to determine which customers are most likely to sign up for a new service. Which technique is best suited for this task?

• Apriori
• Decision Tree
• Sequence
• K-means
• CARMA

Question 5: Which SPSS Modeler node can be used to determine a model’s performance? Select all that apply.

• Evaluation Node
• Analysis Node
• Table Node
• Auto Classifier Node
• Sequence Node

Question 6: Which of the following is NOT a classification or prediction algorithm in SPSS Modeler?

• Linear Regression
• Neural Network
• Logistic Regression
• Discriminant
• Apriori

Question 7: Which SPSS Modeler node is used to specify whether a given field is an input or a target?

• Auto Classifier Node
• Data Audit Node
• Table Node
• Type Node
• Analysis Node

Question 8: Which SPSS Modeler node is useful for exploratory analysis on a data set?

• Analysis Node
• Auto Classifier Node
• Table Node
• Data Audit Node
• Evaluation Node

Question 9: Which SPSS Modeler node is used to both rename fields and exclude fields from the model?

• Restructure Node
• Filter Node
• Partition Node
• Data Audit Node
• Evaluation Node

Question 10: What is the formula for the accuracy metric? TP = true positive, TN = true negative, FP = false positive, and FN = false negative.

• TN / (TN + FP)
• TP / (TP + FN)
• TP / (TP + FP)
• (FP + FN) / (TP + TN + FP + FN)
• (TP + TN) / (TP + TN + FP + FN)

Question 11: Which major data preprocessing step focuses on feature selection and feature extraction?

• Data Integration
• Data Cleaning
• Data Reduction
• Data Audit
• Data Transformation

Question 12: Which SPSS Modeler node is used to identify missing data and screen out potentially problematic fields?

• Auto Data Preparation Node
• Auto Classifier Node
• Restructure Node
• Evaluation Node
• Data Audit Node

Question 13: SPSS Modeler provides automated tools that determine the best algorithm to use for an application. True or false?

• True
• False

Question 14: Which SPSS Modeler node is used for sampling the data set?

• Type Node
• Partition Node
• Data Audit Node
• Filter Node
• Restructure Node

Question 15: Which phase of the data mining process focuses on gathering insights about the data set?

• Data Integration
• Data Understanding
• Business Understanding
• Data Preparation
• Data Preprocessing