PROG 25211 AI and Machine Learning with Python

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Questions and Answers

What percentage of the data is typically used as the training set in machine learning?

90%

50%

80%

70% (correct)

What is the purpose of splitting the data into training and testing sets?

To confuse the model

To evaluate the model's accuracy (correct)

To make training more difficult

To increase the training data

What accuracy range is considered ideal for a well-trained model according to industry standards?

70%-90% (correct)

80%-100%

60%-80%

50%-70%

Why might a model's accuracy exceeding 90% be a cause for concern?

Data overfitting Signup and view all the answers

What step in machine learning involves deciding if the model's accuracy falls within an acceptable range?

Parameter tuning Signup and view all the answers

How does presenting more data during training affect the model's performance?

Improves accuracy Signup and view all the answers

What is the first step when building machine learning algorithms according to the text?

Collecting Data Signup and view all the answers

Which of the following is NOT part of the steps to machine learning mentioned in the text?

Exploratory Data Analysis Signup and view all the answers

What should one do when identifying data for analysis according to the text?

Look for correlations. outliers, and trends Signup and view all the answers

Why is it important to identify where your data is coming from?

To ensure proper ethical practices Signup and view all the answers

Which step involves finding and selecting the data to be used in machine learning analysis?

Collecting Data Signup and view all the answers

What should be done with any duplicate data found during the data preparation step?

Exclude all duplicate data from analysis Signup and view all the answers

What percentage split is typically used for training and testing data in linear regression?

70% training, 30% testing Signup and view all the answers

How does the value of a property relate to the proximity to a transit system according to the text?

It increases Signup and view all the answers

Which location is associated with more expensive properties based on the text?

121.54 Signup and view all the answers

What is the main purpose of splitting data into training and testing sets in linear regression?

To evaluate model performance Signup and view all the answers

How does the number of nearby stores affect the property value according to the text?

Increases it Signup and view all the answers

Why might the value of a property be higher if it is closer to a major city according to the text?

Potential for higher rental income Signup and view all the answers

What type of machine learning algorithm is used to find patterns in data that might not be easily detectable?

Anomaly detection Signup and view all the answers

Which type of ML algorithm represents data sets that are labeled?

Regression Signup and view all the answers

What does Semi-Supervised ML Algorithms represent?

Data sets where some data is not labeled Signup and view all the answers

Which ML algorithm deals with modeling data based on an action and reward?

Reinforcement Learning Signup and view all the answers

What is the purpose of Unsupervised ML Algorithms?

To find patterns in data that are not easily detectable Signup and view all the answers

Which type of ML algorithm represents data clusters that are usually unlabeled?

Clustering Signup and view all the answers

What is the purpose of logistic regression?

Modeling the probability of a discrete outcome Signup and view all the answers

What is the first step in training a linear regression model?

Separating X values from Y values Signup and view all the answers

In linear regression, what changes can be made to improve model accuracy?

Adjusting the random_state parameter Signup and view all the answers

What is the primary goal when evaluating a linear regression model?

Assessing the accuracy of the model Signup and view all the answers

How can logistic regression models differ from linear regression models?

Logistic regression models can handle binary outcomes Signup and view all the answers

What action should be taken to make a prediction using a trained model?

Develop a data frame with desired values Signup and view all the answers

Study Notes

Data Splitting in Machine Learning

Typically, 70-80% of the data is utilized as the training set.
Splitting data into training and testing sets ensures the model is evaluated on unseen data, preventing overfitting and validating its performance.

Model Accuracy and Concerns

An accuracy of 70-90% is generally considered ideal for a well-trained machine learning model.
Accuracy exceeding 90% may indicate potential overfitting, where the model learns noise instead of the underlying data pattern.

Model Evaluation Steps

The step that assesses if a model's accuracy is acceptable occurs during the evaluation phase.
Presenting more data during training typically enhances the model's ability to generalize, improving performance on new data.

Initial Steps in Machine Learning

The first step in building machine learning algorithms involves defining the problem clearly.
Identifying data for analysis requires determining relevance and ensuring it aligns with the problem statement.

Importance of Data Sources

Identifying the source of data is crucial for understanding its quality, reliability, and possible bias, impacting the model's performance.

Data Preparation Steps

The data preparation step includes selecting and finding relevant data for analysis.
Duplicate data should be removed to maintain data integrity and improve the accuracy of the model.

Linear Regression Data Split

In linear regression, a standard split of 70-30% is common for training and testing datasets.

Property Value Factors

Proximity to a transit system can significantly increase a property's value due to accessibility.
Properties located closer to major cities often have higher prices due to demand and development opportunities.
Nearby stores can positively impact property values, as they enhance convenience and livability.

Machine Learning Algorithm Types

Unsupervised learning algorithms are used to detect patterns in unlabeled data.
Supervised learning refers to algorithms that operate with labeled datasets, utilizing known inputs and outputs.
Semi-supervised ML algorithms blend both labeled and unlabeled data to improve learning efficiency.
Algorithms that model actions based on rewards are termed reinforcement learning.

Logistic Regression

Logistic regression is designed to predict binary outcomes, determining probabilities for categorical outcomes.
The initial step in training a linear regression model involves selecting appropriate features and preparing the input data.
Improving model accuracy in linear regression can include feature selection, normalization, or data transformation.

Goals and Predictions in Regression

The primary goal when evaluating a linear regression model is to minimize prediction error.
Logistic regression models differ from linear regression as they predict likelihoods instead of continuous values.
To make a prediction with a trained model, input data must be passed through the model to generate an output based on learned patterns.

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Description

Learn about linear and logistic regression in machine learning using Python with Jonathan Penava at Sheridan College. The quiz covers the steps to machine learning, including collecting data, preparing the data, and choosing algorithms.