commit 4f6bc191bc4dcb2b37ab2e5764f99e4e112e2a09
parent e4912234bd3d0777fd0a2732e542be13c75fb150
Author: Andrew <andrewlaack1@gmail.com>
Date: Fri, 10 May 2024 21:09:37 -0500
Today's notes
Diffstat:
3 files changed, 31 insertions(+), 0 deletions(-)
diff --git a/KNearestNeighbor.md b/KNearestNeighbor.md
@@ -0,0 +1,19 @@
+:ml:
+# k Nearest Neighbor
+
+ML CH1
+
+## Notes
+
+**Definition:** k nearest neighbor is the idea of using the k nearest elements of some set to derive some information.
+
+In ml, this can be used used to find the k nearest neighbor regression of a sample using an instance based approach where you would find the k nearest values and average them. This would then be the prediction for the sample.
+
+Using sklearn you can specify to load k nearest neighbor as follows:
+
+```python
+
+from sklearn.neighbors import KNeighboarsRegressor
+model = KNeighborsRegressor(n_neighbors=3)
+
+```
diff --git a/LinearRegression.md b/LinearRegression.md
@@ -8,3 +8,12 @@ ML L2 - Also referred to as ordinary least squares
**Definition:** Fitting a straight line to data which allows for arbitrary inputs in the valid domain but not necessarily in the training set, to get accurate outputs.
The goal is to find a $\theta$ (parameters) that minimizes $J(\theta)=\frac{1}{2}\sum_{i=1}^{m}(h(x_i) - y_i)^2$. This is called the cost function.
+
+To load linear regression using SkiLearn do as follows:
+
+```python3
+
+from sklearn.linear_model import LinearRegression
+model = LinearRegression()
+
+```
diff --git a/MachineLearning.md b/MachineLearning.md
@@ -10,6 +10,8 @@ Links to ML Notes
1. How do I create new ML models
2. Create chess ML program
- [[ReinforcementLearning.md]]
+3. Create a walking model
+ - [[ReinforcementLearning.md]]
## Good Info
@@ -59,6 +61,7 @@ Concepts:
[[Feature.md]]
[[OfflineLearning.md]]
[[OnlineLearning.md]]
+[[KNearestNeighbor.md]]
To do:
