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data mining, Evaluation Metrics, Model Selection - Coggle Diagram
data mining
clustering
autoencoder
Basic autoencoder
Denoising autoencoder
Sparse autoencoder
Contractive autoencoder
Marginalized denoising autoencoder
Convolutional autoencoder
Variational autoencoder (VAE)
Wassersterin autoencoder
DBSCAN
HDBSCAN(Hierarchical DBSCAN)
OPTICS (Ordering Points To Identify the Clustering Structure)
Graph-based Clustering
Spectral Clustering
K-modes
K-prototypes
evaluation
Gower distance
Disagreement distance
External Evaluation
Purity
Rand Index
Jaccard Index
Mutual Information
Internal Evaluation
Dunn Index
Silhouette coefficient
Davies-Bouldin Index
classification评估
precision 从人出发 全P
recall 从样本出发 最后一个N不一样
F1-score
ROC
ensemble learning
bagging
boosting
Adaptive Boosting
AdaBoost
Real AdaBoost
LogitBoost
Gradient boosting
XGBoost
LightGBM
CatBoost (Categorical Boosting
stacking
time series forecasting
methods
Statistical Methods
Autoregressive） - AR
（Autoregressive Moving Average） - ARMA
Autoregressive Integrated Moving Average） - ARIMA
Seasonal Autoregressive Integrated Moving Average） - SARIMA
Exponential Smoothing） - ES
Gated Recurrent Unit） - GRU
Moving Average） - MA
machine learning models
Decision Tree
Random Forest
XGBoost
LightGBM
Support Vector
Regression (SVR)
Linear Regression
Ridge Regression
Lasso Regression
Elastic Net
Deep Learning (DL)
MLPs
RNNs
CNNs
N-BEATS
DeepAR
Autoencoders
Attention
Mechanism
Evaluation Metrics
MSE
RMSE
MAE
MAPE
Pearson’s Correlation
Spearman’s Rank Correlation
Model Selection
Autocorrelation
Function (ACF)
Partial Autocorrelation Function (PACF)