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Questions and Answers
What is business analytics?
What is business analytics?
What is machine learning?
What is machine learning?
Which of the following are related terms to machine learning? (Select all that apply)
Which of the following are related terms to machine learning? (Select all that apply)
What is big data?
What is big data?
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What is data science?
What is data science?
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There are many different methods in data science because it has a wide range of applications.
There are many different methods in data science because it has a wide range of applications.
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Match the following chapters with their topics:
Match the following chapters with their topics:
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What is the curse of dimensionality?
What is the curse of dimensionality?
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What is principal component analysis (PCA)?
What is principal component analysis (PCA)?
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What is logistic regression?
What is logistic regression?
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Neural networks are a subset of machine learning techniques.
Neural networks are a subset of machine learning techniques.
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What are ensembles in machine learning?
What are ensembles in machine learning?
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What are automated machine learning methods?
What are automated machine learning methods?
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Study Notes
Introduction to Business Analytics and Machine Learning
- Business analytics involves using data analysis and statistical methods to inform business decisions.
- Machine learning is a subset of AI, focusing on algorithms that improve automatically through experience.
- Big data refers to vast and complex data sets that traditional processing systems cannot handle efficiently.
- Data science combines domain expertise, programming, and knowledge of mathematics and statistics to extract insights from data.
Machine Learning Process Overview
- The machine learning process includes phases of problem definition, data collection, data preparation, model training, evaluation, and deployment.
- Overfitting occurs when a model learns noise from the training data rather than the underlying pattern, reducing its predictive power.
- Ethical practice in machine learning is critical, addressing biases, data privacy, and ethical implications of automated decisions.
Data Exploration and Visualization
- Data visualization is essential for understanding data distributions, trends, and patterns.
- Common visualization types include bar charts, line charts, scatter plots, and multidimensional visualizations.
- Specialized visualizations can help in specific contexts or industries, enhancing interpretability.
Dimension Reduction Techniques
- The curse of dimensionality describes challenges faced with high-dimensional data, such as increased computational cost and overfitting.
- Methods like Principal Component Analysis (PCA) help reduce the number of variables while retaining as much information as possible.
- Converting categorical variables to numerical formats or reducing the number of categories can help simplify analysis.
Performance Evaluation of Models
- Evaluating predictive performance is crucial to understand how well a model predicts outcomes using metrics such as accuracy, precision, recall, and F1 score.
- Judging classifier performance involves comparing true positive rates, false positive rates, and overall accuracy.
Prediction and Classification Methods
- Multiple linear regression relates a dependent variable to multiple independent variables to predict outcomes.
- k-Nearest Neighbors (k-NN) is a non-parametric, instance-based learning algorithm for classification and regression tasks.
- The Naive Bayes classifier applies Bayes' theorem and assumes independence among predictors, making it efficient for large datasets.
Trees in Machine Learning
- Classification trees split data into subsets based on feature values for decision-making; regression trees predict continuous outcomes.
- Random forests and boosted trees are ensemble methods that improve prediction accuracy by combining multiple models.
Logistic Regression
- Logistic regression is a statistical method for analyzing datasets with one or more independent variables that determine an outcome.
- It is effective for binary classification problems and can be extended to multinomial classification.
Neural Networks and Deep Learning
- Neural networks mimic the human brain's structure, consisting of interconnected layers that process data similarly to neurons.
- Deep learning, a type of machine learning, utilizes large neural networks with many layers to analyze various data types, such as images and text.
Discriminant Analysis
- Discriminant analysis classifies observations based on predictor variables by finding the linear combinations that separate classes.
- It can handle unequal misclassification costs and is extendable to situations with more than two classes.
Model Generation and Combination
- Ensemble methods combine predictions from multiple models to enhance performance and robustness.
- Automated Machine Learning (AutoML) seeks to automate the end-to-end process of applying machine learning, making it accessible to non-experts.
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Description
This quiz covers foundational concepts from the introduction chapter of Business Analytics, focusing on machine learning, big data, and data science. It is designed to test comprehension of key terms and their relationships in the context of business analytics. Ideal for beginners looking to get acquainted with essential terminology.
