Cheatsheet - Achronus/Machine-Learning-101 GitHub Wiki

Table of Contents

• General
• Scikit-Learn (Sklearn)
  • General
  • Data Preprocessing
  • Model Selection
  • Accuracy & Predictions
  • Models
• Keras

General

• import numpy as np
• import pandas as pd
• import matplotlib.pyplot as plt

Scikit-Learn (Sklearn)

General

• .fit() - used to find the internal parameters of a model

• .transform() - used to map new or existing values to data

• .fit_transform() - does both fit and transform

• .predict() - used to make predictions

• from xgboost import XGBClassifier - XGBoost gradient boosting software

Data Preprocessing

• from sklearn.preprocessing import ...
  • LabelEncoder - used for categorical data
  • OneHotEncoder - used for dummy variables
  • StandardScaler - used for standardising data
  • MinMaxScaler - Used for normalising data
  • Imputer - Used to replace empty spaces/missing data within a dataset

Model Selection

• from sklearn.model_selection import ...
  • train_test_split - used for splitting data into test sets and training sets
  • cross_val_score - used for K-Fold Cross Validation
  • GridSearchCV - used for grid search (tuning models)

Accuracy & Predictions

• from sklearn.metrics import confusion_matrix - used to identify the accuracy of a trained model

Models

• from sklearn.preprocessing import PolynomialFeatures - Used for creating Polynomial Regressions

• from sklearn.svm import ...

  • SVC - the model class for Support Vector Classification & Kernel SVM
  • SVR - the model class for Support Vector Regression

• from sklearn.linear_model import ...

  • LinearRegression - used for Linear Regressions (single and multiple variables).
  • LogisticRegression - used for Logistic Regressions

• from sklearn.tree import ...

  • DecisionTreeRegressor - used for Decision Tree Regression
  • DecisionTreeClassifier - used for Decision Tree Classification

• from sklearn.ensemble import ...

  • RandomForestRegressor - used for Random Forest Regression
  • RandomForestClassifier - used for Random Forest Classification

• from sklearn.neighbors import KNeighborsClassifier - K-Neighbours Classification model

• from sklearn.naive_bayes import GaussianNB - Naive Bayes model

• import scipy.cluster.hierarchy as sch - A popular library that can be used for dendrogram creation in Hierarchical Clustering

• from sklearn.cluster import ...

  • AgglomerativeClustering - A Hierarchical Clustering Model
  • KMeans - K-Means clustering model

• from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA - Linear Discriminant Analysis model

• from sklearn.decomposition import ...

  • PCA - Principal Component Analysis model
  • KernelPCA - Kernel PCA model

Keras

• from keras.models import Sequential - basic building block to creating a model

• from keras.layers import ...

  • Dense - basic function for linear models
  • Dropout - used to add dropout to layers
  • Flatten - used to flatten convolutional layers
  • Conv2D - a basic convolutional layer
  • MaxPooling2D - used to apply max pooling to a convolutional layer

• from keras.wrappers.scikit_learn import KerasClassifier - used to wrap a sequential model to allow the model to be fit to datasets