Linear Regression:
- Goal: Predict a continuous value using a linear relationship.
- Steps:
- Collect data on independent and dependent variables.
- Fit a line to the data using least squares regression.
- Use the line to predict future values.
Logistic Regression:
- Goal: Predict a binary outcome (0 or 1) using a logistic function.
- Steps:
- Collect data on independent variables and binary outcomes.
- Fit a logistic function to the data using maximum likelihood estimation.
- Use the function to predict future outcomes.
Decision Trees:
- Goal: Classify data into multiple categories using a tree-like structure.
- Steps:
- Collect data on attributes and categories.
- Build a tree where each node represents an attribute and each branch represents a decision.
- Classify new data by traversing the tree.
Random Forests:
- Goal: Improve accuracy by combining multiple decision trees.
- Steps:
- Create multiple decision trees using different subsets of data.
- Combine the predictions of the trees by majority vote or averaging.
Support Vector Machines (SVM):
- Goal: Classify data into two or more categories by finding the best hyperplane that separates the data.
- Steps:
- Collect data on attributes and categories.
- Find the hyperplane that maximizes the margin between the categories.
- Classify new data points by their position relative to the hyperplane.
K-Nearest Neighbors (KNN):
- Goal: Classify or predict data by finding the most similar examples in the training data.
- Steps:
- Collect data on attributes and categories or values.
- For a new data point, find the K nearest neighbors in the training data.
- Classify the new data point based on the majority vote or weighted average of the neighbors.
Python Example:
Overview of Commonly Used Machine Learning Algorithms
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
data = pd.read_csv('data.csv')
# Linear Regression
model = LinearRegression()
model.fit(data[['x']], data['y'])
# Logistic Regression
model = LogisticRegression()
model.fit(data[['x']], data['y'])
# Decision Tree
model = DecisionTreeClassifier()
model.fit(data[['x']], data['y'])
# Random Forest
model = RandomForestClassifier()
model.fit(data[['x']], data['y'])
# SVM
model = SVC()
model.fit(data[['x']], data['y'])
# KNN
model = KNeighborsClassifier()
model.fit(data[['x']], data['y'])