[ 机器学习 - 吴恩达 ] Linear regression with one variable | 2-1 Model representation
Housing Prices
- Supervised Learning: Given the "right answer" for each example in the data
- Regression Problem: Predict real-valed (实数) output
Training set of housing prices (Portland, OR)
| Size in \(feet^2\) (x) | Price ($) in 1000's (y) |
|---|---|
| 2104 | 460 |
| 1416 | 232 |
| 1534 | 315 |
| 852 | 178 |
Notation:
- m = Number of training examples
- x's = "input" variable / features
- y's = "output" variable / "target" variable
- (x, y) - one training example
- (\(x^(i)\), \(y^(i)\)) - \(i^{th}\) traning example
"h: hypothesis (假设)" maps from x's to y's
How do we represent \(h\)? (在房价数据集上)
\[h_\theta = \theta_0 + \theta_1x \]Shorthand: \(h(x)\)
"Linear regression with one variable. \(x\)"
or "Univariable linear regression."