Here's a comprehensive list of commonly used machine learning models in scikit-learn, with descriptions and syntax grouped by type. Mathematical intuition is included where applicable. Feel free to print these for reference!
1. Linear Models
These models assume a linear relationship between the features and the target variable.
1.1 Linear Regression
Syntax:
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)Description: Predicts a continuous target variable by fitting a linear relationship between input features and the target.
Equation: [ y = w_0 + w_1 x_1 + w_2 x_2 + \dots + w_n x_n ]
1.2 Logistic Regression
Syntax:
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)Description: Used for binary or multi-class classification by modeling the log odds of the probability of an outcome.
Equation:
2. Decision Tree Models
2.1 Decision Tree Classifier
Syntax:
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
model.fit(X_train, y_train)Description: A tree-based algorithm where decisions are made by splitting the dataset into subsets based on feature values.
Equation: Non-parametric, splits data by minimizing impurity (Gini or Entropy).
2.2 Decision Tree Regressor
Syntax:
from sklearn.tree import DecisionTreeRegressor
model = DecisionTreeRegressor()
model.fit(X_train, y_train)Description: Like the classifier, but predicts continuous target values.
3. Ensemble Methods
3.1 Random Forest Classifier
Syntax:
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)Description: Combines multiple decision trees to improve classification accuracy. Each tree is trained on a random subset of the data.
3.2 Random Forest Regressor
Syntax:
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()
model.fit(X_train, y_train)Description: Similar to Random Forest Classifier, but used for regression tasks.
3.3 Gradient Boosting Classifier
Syntax:
from sklearn.ensemble import GradientBoostingClassifier
model = GradientBoostingClassifier()
model.fit(X_train, y_train)Description: Builds trees sequentially, where each tree corrects the mistakes of the previous one by minimizing the loss.
3.4 Gradient Boosting Regressor
Syntax:
from sklearn.ensemble import GradientBoostingRegressor
model = GradientBoostingRegressor()
model.fit(X_train, y_train)Description: Same as Gradient Boosting Classifier, but for regression tasks.
4. Support Vector Machines (SVM)
4.1 SVM Classifier
Syntax:
from sklearn.svm import SVC
model = SVC()
model.fit(X_train, y_train)Description: Classifies data by finding a hyperplane that best separates the classes. Can be extended to non-linear decision boundaries using kernels.
Equation:
4.2 SVM Regressor
Syntax:
from sklearn.svm import SVR
model = SVR()
model.fit(X_train, y_train)Description: Similar to SVC but used for regression tasks, fits a hyperplane in feature space.
5. Naive Bayes
5.1 Gaussian Naive Bayes
Syntax:
from sklearn.naive_bayes import GaussianNB
model = GaussianNB()
model.fit(X_train, y_train)Description: Assumes features are normally distributed and applies Bayes' theorem for classification.
Equation:
5.2 Multinomial Naive Bayes
Syntax:
from sklearn.naive_bayes import MultinomialNB
model = MultinomialNB()
model.fit(X_train, y_train)Description: Used for discrete data, especially for text classification like spam filtering.
6. K-Nearest Neighbors (KNN)
6.1 KNN Classifier
Syntax:
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train, y_train)Description: Classifies data points based on the majority label of the closest data points (neighbors).
Equation: No parametric equation; classification based on distance metrics (e.g., Euclidean).
6.2 KNN Regressor
Syntax:
from sklearn.neighbors import KNeighborsRegressor
model = KNeighborsRegressor(n_neighbors=5)
model.fit(X_train, y_train)Description: Similar to KNN Classifier, but predicts continuous values based on nearest neighbors.
7. Clustering Models
7.1 K-Means Clustering
Syntax:
from sklearn.cluster import KMeans
model = KMeans(n_clusters=3)
model.fit(X_train)Description: Partitions data into k clusters, where each data point belongs to the cluster with the nearest mean.
Equation:
7.2 DBSCAN
Syntax:
from sklearn.cluster import DBSCAN
model = DBSCAN(eps=0.5, min_samples=5)
model.fit(X_train)Description: Density-based clustering that groups together closely packed points and marks points in low-density regions as outliers.
8. Neural Networks
8.1 MLP Classifier (Multi-Layer Perceptron)
Syntax:
from sklearn.neural_network import MLPClassifier
model = MLPClassifier(hidden_layer_sizes=(100,))
model.fit(X_train, y_train)Description: Fully connected feedforward neural network for classification tasks.
8.2 MLP Regressor
Syntax:
from sklearn.neural_network import MLPRegressor
model = MLPRegressor(hidden_layer_sizes=(100,))
model.fit(X_train, y_train)Description: Similar to MLP Classifier but used for regression tasks.
These are the main models you can use in scikit-learn, organized by type, with corresponding syntax and brief descriptions. You can print these along with the equations and stick them on your wall for easy reference!