Machine Learning with R Cognitive Class Exam Quiz Answers

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Machine Learning with R Cognitive Class Certificate Answers

Module 1 – Machine Learning vs Statistical Modeling

Question 1: Machine Learning was developed shortly (within the same century) as statistical modelling, therefore adopting many of its practices.

True
False

Question 2: Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data.

True
False

Question 3: Machine Learning is applied in current technologies, such as:

Trend Prediction (ex. House Price Trends)
Gesture Recognition (ex. Xbox Connect)
Facial Recognition (ex. Snapchat)
A and B, but not C
All of the above

Module 2 – Supervised Learning I

Question 1: In K-Nearest Neighbors, which of the following is true:

A very high value of K (ex. K = 100) produces a model that is better than a very low value of K (ex. K = 1)
A very high value of K (ex. K = 100) produces an overly generalised model, while a very low value of k (ex. k = 1) produces a highly complex model.
A very low value of K (ex. K = 1) produces an overly generalised model, while a very high value of k (ex. k = 100) produces a highly complex model.
All of the Above

Question 2: A difficulty that arises from trying to classify out-of-sample data is that the actual classification may not be known, therefore making it hard to produce an accurate result.

True
False

Question 3: When building a decision tree, we want to split the nodes in a way that decreases entropy and increases information gain.

True
False

Module 3 – Supervised Learning II

Question 1: Which of the following is generally true about the evaluation models: Train and Test on the Same Dataset and Train/Test Split.

Train and Test on the Same Dataset has a high training accuracy and high out-of-sample accuracy, while Train/Test Split has a low training accuracy and low out-of-sample accuracy.
Train and Test on the Same Dataset has a low training accuracy and high out-of-sample accuracy, while Train/Test Split has a high training accuracy and low out-of-sample accuracy.
Train and Test on the Same Dataset has a high training accuracy and low out-of-sample accuracy, while Train/Test Split has a low training accuracy and high out-of-sample accuracy.
Train and Test on the Same Dataset has a low training accuracy and low out-of-sample accuracy, while Train/Test Split has a high training accuracy and high out-of-sample accuracy.

Question 2: Which of the following is true about bias and variance?

Having a high bias underfits the data and produces a model that is overly complex, while having high variance overfits the data and produces a model that is overly generalized.
Having a high bias underfits the data and produces a model that is overly generalized, while having high variance overfits the data and produces a model that is overly complex.
Having a high bias overfits the data and produces a model that is overly complex, while having high variance underfits the data and produces a model that is overly generalized.
Having a high bias overfits the data and produces a model that is overly generalized, while having high variance underfits the data and produces a model that is overly complex.

Question 3: Root Mean Squared Error is the most popular evaluation metric out of the three discussed, because it produces the same units as the response vector, making it easy to relate information.

True
False

Module 4 – Unsupervised Learning

Question 1: What are some disadvantages that K-means clustering presents?

Updating can occur even though there is a possibility of a centroid not having data points in its group
K-means clustering is generally slower, compared to many other clustering algorithms
There is high bias in the models, due to where the centroids are initiated
None of the above

Question 2: Decision Trees tend to have high bias and low variance, which Random Forests fix.

True
False

Question 3: A Dendrogram can only be read for Agglomerative Hierarchical Clustering, not Divisive Hierarchical Clustering.

True
False

Module 5 – Dimensionality Reduction & Collaborative Filtering

Question 1: Filters produce a feature set that does not contain assumptions based on the predictive model, making it a useful tool to expose relationships between features.

True
False

Question 2: Principle Components Analysis retains all information during the projection process of higher order features to lower orders.

True
False

Question 3: Which of the following is not a challenge to a recommendation system that uses collaborative filtering?

Diversity Sheep
Shilling Attacks
Scalability
Synonyms

Machine Learning with R Final Exam Answers – Cognitive Class

Question 1: Randomness is important in Random Forests because it allows us to have distinct, different trees that are based off of different data.

True
False

Question 2: When building a decision tree, we want to split the nodes in a way that increases entropy and decreases information gain.

True
False

Question 3: Which of the following is true?

A high value of K in KNN creates a model with low bias and high variance
An observation must contain values for all features
A categorical value cannot be numeric
None of the above

Question 4: In terms of Bias and Variance, Variance is the inconsistency of a model due to small changes in the dataset.

True
False

Question 5: Which is the definition of entropy?

The purity of each node in a random forest.
Information collected that can increase the level of certainty in a particular prediction.
The information that is used to randomly select a subset of data.
The amount of information disorder in the data.

Question 6: Which of the following is true about hierarchical linkages?

Average linkage is the average distance of each point in one cluster to every point in another cluster
Complete linkage is the shortest distance between a point in two clusters
Centroid linkage is the distance between two randomly generated centroids in two clusters
Single linkage is the distance between any points in two clusters

Question 7: In terms of Bias and Variance, Variance is the inconsistency of a model due to small changes in the dataset.

True
False

Question 8: Which is true about bootstrapping?

All data points must be used when bootstrapping is applied
The data points are randomly selected with replacement
The data points are randomly selected without replacement
It is the same as bagging

Question 9: Machine Learning is still in early development and does not have much of an impact on the current society.

True
False

Question 10: In comparison to supervised learning, unsupervised learning has:

Less tests
More models
A better controlled environment
More tests, but less models

Question 11: Outliers are points that are classified by Density-Based Clustering that do not belong to any cluster.

True
False

Question 12: Which of the following is false about Linear Regression?

It does not require tuning parameters
It is highly interpretable
It is fast
It has a low variability on predictive accuracy

Question 13: Machine Learning uses algorithms that can learn from data without relying on standard programming practices.

True
False

Question 14: Which of the following are types of supervised learning?

Clustering
Regression
Classification
Both A and B

Question 15: A Bottom Up version of hierarchical clustering is known as Divisive clustering. It is a more popular method than the agglomerative method.

True
False

Question 16: Which is NOT a specific outcome of how Dimensionality Performance improves production?

Highlights the main linear technique called Principle Components Analysis.
Creates step-wise regression.
Reduces number of features to be considered.
Highlights relevant variables only and omits irrelevant ones.

Question 17: Feature Selection is the process of selecting the variables that will be projected from a high-order dimension to a lower one.

True
False

Question 18: Hierarchical Clustering is one of the three main algorithms for clustering along with K-Means and Density Based Clustering.

True
False

Question 19: Which one is NOT a feature of Dimensionality Reduction?

It can be divided into two subcategories called Feature Selection and Feature Extraction
Removal of an “outsider” from the least cohesive cluster.
Feature Selection includes Wrappers, Filters, and Embedded.
Feature Extraction includes Principle Components Analysis.
It reduces the number of variables/features in review.

Question 20: Low bias tends to create overly generalized models, which can cause a loss of relevant relations between the features and target output. When a model has low bias, we say that it “under fits” the data.

True
False

Introduction to Machine Learning with R

Machine learning (ML) is a field of artificial intelligence (AI) that enables computers to learn from data and make decisions without being explicitly programmed. R, a powerful language for statistical computing and graphics, offers a rich ecosystem for implementing machine learning algorithms. This guide will introduce you to the basics of machine learning using R, covering key concepts, tools, and examples.

1. Getting Started with R

1.1 Installing R and RStudio

To start using R for machine learning, you’ll need to install R and RStudio:

R: Download and install R from the CRAN website.
RStudio: Download and install RStudio, an integrated development environment (IDE) for R, from the RStudio website.

1.2 Basic R Commands

Familiarize yourself with basic R commands and data structures

2. Machine Learning Basics

2.1 Types of Machine Learning

Supervised Learning: Learning from labeled data (e.g., classification and regression).
Unsupervised Learning: Learning from unlabeled data (e.g., clustering and association).
Reinforcement Learning: Learning through rewards and penalties.

2.2 Common Algorithms

Linear Regression: Predicting a continuous target variable.
Logistic Regression: Classifying binary outcomes.
Decision Trees: Modeling decisions and their possible consequences.
Random Forest: Ensemble learning method for classification and regression.
Support Vector Machines (SVM): Classifying data by finding the optimal hyperplane.
k-Nearest Neighbors (k-NN): Classifying data based on the nearest training examples.

3. Preparing Data

3.1 Loading Data

Load data from various sources such as CSV files, databases, or built-in datasets

3.2 Exploring Data

Use summary statistics and visualizations to understand your data

3.3 Preprocessing Data

Prepare your data for modeling by handling missing values, encoding categorical variables, and scaling features

4. Building Machine Learning Models

4.1 Splitting Data

Split the data into training and testing sets

4.2 Training Models

Train various machine learning models using different algorithms

4.3 Evaluating Models

Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score

5. Advanced Topics

5.1 Cross-Validation

Improve model reliability with cross-validation

5.2 Hyperparameter Tuning

Optimize model performance by tuning hyperparameters

5.3 Ensemble Methods

Combine multiple models to improve performance

6. Practical Example

6.1 Iris Dataset

Let’s apply the concepts learned to the Iris dataset, a classic dataset in machine learning

Conclusion

Machine learning with R is a powerful approach to data analysis and predictive modeling. By understanding the basics of data preparation, model training, and evaluation, you can build effective machine learning models. This guide provides a foundation for exploring more advanced techniques and applications in machine learning.