MM - Regression and Classification using Scikit-learn

Creating mind maps for key concepts in regression and classification using Scikit-learn can help you quickly recall the material. Here are some keywords and short sentences you can use for your mind maps:

Regression

Linear Regression

• Definition: Linear relationship between variables.
• Formula: (y = mx + c)
• Libraries: LinearRegression
• Example: Predict house prices.
• Key Functions: fit(), predict()
• Metrics: Mean Squared Error (MSE), R-squared ((R^2))

Multiple Linear Regression

• Multiple Variables: Use more than one predictor.
• Libraries: LinearRegression
• Example: Predict sales based on marketing spend.
• Key Functions: fit(), predict()
• Metrics: MSE, (R^2)

Logistic Regression

• Binary Classification: Predict probability of class.
• Formula: Sigmoid function (P(y=1) = \frac{1}{1+e^{-z}})
• Libraries: LogisticRegression
• Example: Predict if an email is spam.
• Key Functions: fit(), predict(), predict_proba()
• Metrics: Confusion Matrix, Classification Report (Precision, Recall, F1-score)

Classification

Naive Bayes

• Definition: Based on Bayes' theorem.
• Types: GaussianNB, MultinomialNB, BernoulliNB.
• Libraries: GaussianNB, MultinomialNB, BernoulliNB
• Example: Text classification.
• Key Functions: fit(), predict(), predict_proba()
• Metrics: Confusion Matrix, Classification Report

Decision Trees

• Definition: Tree-like model of decisions.
• Libraries: DecisionTreeClassifier
• Example: Predict customer churn.
• Key Functions: fit(), predict()
• Visualization: plot_tree()
• Metrics: Confusion Matrix, Classification Report

General Concepts

Data Preprocessing

• Scaling: StandardScaler
• Encoding: OneHotEncoder
• Imputation: SimpleImputer

Model Evaluation

• Train/Test Split: train_test_split
• Metrics: Accuracy, Precision, Recall, F1-score, MSE, (R^2)

Scikit-learn Basics

• Installation: pip install scikit-learn
• Datasets: load_iris(), load_digits()
• Common Classes: LinearRegression, LogisticRegression, GaussianNB, DecisionTreeClassifier
• Key Methods: fit(), predict(), score()

Mind Map Example Structure

1. Regression
   - Linear Regression
     - Definition
     - Formula
     - Libraries
     - Example
     - Key Functions
     - Metrics
   - Multiple Linear Regression
     - Multiple Variables
     - Libraries
     - Example
     - Key Functions
     - Metrics
   - Logistic Regression
     - Binary Classification
     - Formula
     - Libraries
     - Example
     - Key Functions
     - Metrics
2. Classification
   - Naive Bayes
     - Definition
     - Types
     - Libraries
     - Example
     - Key Functions
     - Metrics
   - Decision Trees
     - Definition
     - Libraries
     - Example
     - Key Functions
     - Visualization
     - Metrics
3. General Concepts
   - Data Preprocessing
     - Scaling
     - Encoding
     - Imputation
   - Model Evaluation
     - Train/Test Split
     - Metrics
   - Scikit-learn Basics
     - Installation
     - Datasets
     - Common Classes
     - Key Methods

Use these keywords and short sentences to structure your mind map. Each branch should be a topic or subtopic, making it easy to visually organize and recall the information. Let me know if you need further assistance!