Scikit-Learn：使用Python进行预测数据分析的机器学习库

# Scikit Learn **Machine Learning** Library for **Predictive Data Analysis** using **Python** ### Y = X * B1 + B0 ## Coefficients ### B1 : Slope - Gradient | **Steepness** in Line | **Direction** of Line | **Weight** ### B0 : Intercept - Bias | Constant | The Place where **Regression Line** Intersects the Y Axis (Value of Y when X = 0) ### R² : Coefficient of Determination - The **Variance** of the Data captured by the Model (0.7 to 0.9 is Good value for R²) - Large R² indicates a Better Fit (The Model can Explain the Variation of Predictions with Actual in much Better Way) - R² = 1 corresponds to SSR = 0 (**Perfect Fit**) - Low R² causes **Underfitting** - High R² resultss into **Overfitting** ### Residuals - Actual - Prediction ## Linear Regression **Learning** a Linear Regression Model means Estimating the Values of the **Coefficient** **Predict** the Value of a **Feature** based on the Value of Another Feature. 1. Simple Linear Regression 2. Multiple Linear Regression 3. Polynomial Linear Regression 4. Advanced Linear Regression