PROG 25211 AI and Machine Learning with Python

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.

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.