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MM - Regression - Linear Regression, Logistic Regression.

Sure! Here's a list of keywords and short sentences to help you create mind maps for Linear Regression and Logistic Regression.

Linear Regression

Core Concepts

  • Dependent Variable (y): The outcome being predicted.
  • Independent Variables (x1, x2, ...): Predictors used to estimate y.
  • Linear Equation: $$( y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \ldots + \beta_nx_n + \epsilon )$$
  • Intercept ((\beta_0)): Value of y when all x's are zero.
  • Coefficients ((\beta)): Effect of each predictor on y.
  • Error Term ((\epsilon)): Difference between predicted and actual values.

Key Steps

  • Data Preprocessing: Clean and prepare data.
  • Train-Test Split: Divide data into training and testing sets.
  • Model Training: Fit linear regression model on training data.
  • Prediction: Use model to predict y on test data.
  • Evaluation: Assess model performance using MSE, R-squared.

Keywords

  • NumPy: Numerical operations.
  • Pandas: Data manipulation.
  • Scikit-learn: Machine learning.
  • Mean Squared Error (MSE): Measure of prediction accuracy.
  • R-squared: Proportion of variance explained by the model.

Logistic Regression

Core Concepts

  • Binary Outcome (0/1): Dependent variable is categorical.
  • Logistic Function: Maps predicted values to probabilities ( P(y=1|x) ).
  • Log Odds: Linear combination of predictors.
  • Odds Ratio: Exponentiated coefficients, measure change in odds.
  • Sigmoid Curve: S-shaped curve mapping linear output to (0,1).

Key Steps

  • Data Preprocessing: Clean and prepare data.
  • Train-Test Split: Divide data into training and testing sets.
  • Model Training: Fit logistic regression model on training data.
  • Prediction: Use model to predict probabilities and classes on test data.
  • Evaluation: Assess model performance using accuracy, precision, recall, F1 score, confusion matrix.

Keywords

  • NumPy: Numerical operations.
  • Pandas: Data manipulation.
  • Scikit-learn: Machine learning.
  • Accuracy: Overall correctness.
  • Precision: True positives out of predicted positives.
  • Recall: True positives out of actual positives.
  • F1 Score: Harmonic mean of precision and recall.
  • Confusion Matrix: Table of true vs. predicted classes.

Visual Mind Map

For each section, start with a central node labeled "Linear Regression" or "Logistic Regression". Branch out into the following sub-nodes:

Linear Regression Mind Map

  1. Core Concepts

    • Dependent Variable (y)
    • Independent Variables (x1, x2, ...)
    • Linear Equation
    • Intercept ((\beta_0))
    • Coefficients ((\beta))
    • Error Term ((\epsilon))

  2. Key Steps

    • Data Preprocessing
    • Train-Test Split
    • Model Training
    • Prediction
    • Evaluation

  3. Keywords

    • NumPy
    • Pandas
    • Scikit-learn
    • Mean Squared Error (MSE)
    • R-squared

Logistic Regression Mind Map

  1. Core Concepts

    • Binary Outcome (0/1)
    • Logistic Function
    • Log Odds
    • Odds Ratio
    • Sigmoid Curve

  2. Key Steps

    • Data Preprocessing
    • Train-Test Split
    • Model Training
    • Prediction
    • Evaluation

  3. Keywords

    • NumPy
    • Pandas
    • Scikit-learn
    • Accuracy
    • Precision
    • Recall
    • F1 Score
    • Confusion Matrix

Using these keywords and short sentences, you can create a detailed and organized mind map that will help you quickly recall the essential elements of Linear and Logistic Regression.