監督學習模型是指在訓練過程中,使用帶有正确答案的标記資料來進行學習。常見的監督學習模型包括邏輯回歸、決策樹、支援向量機、樸素貝葉斯分類器、神經網絡等。最近流行的監督學習模型還包括深度學習模型,如卷積神經網絡和循環神經網絡。
下面給出一些示例與實作方法:
邏輯回歸:https://github.com/trekhleb/homemade-machine-learning
決策樹:https://github.com/scikit-learn/scikit-learn
支援向量機:https://github.com/scikit-learn/scikit-learn
随機森林:https://github.com/scikit-learn/scikit-learn
GBDT:https://github.com/scikit-learn/scikit-learn
XGBoost:https://github.com/dmlc/xgboost
LightGBM:https://github.com/microsoft/LightGBM
CatBoost:https://github.com/catboost/catboost
神經網絡:https://github.com/tensorflow/tensorflow
卷積神經網絡:https://github.com/tensorflow/tensorflow
循環神經網絡:https://github.com/tensorflow/tensorflow
雙向循環神經網絡:https://github.com/tensorflow/tensorflow
全連接配接神經網絡:https://github.com/tensorflow/tensorflow
自編碼器:https://github.com/tensorflow/tensorflow
聚類:https://github.com/scikit-learn/scikit-learn
K-Means:https://github.com/scikit-learn/scikit-learn
高斯混合模型:https://github.com/scikit-learn/scikit-learn
譜聚類:https://github.com/scikit-learn/scikit-learn
層次聚類:https://github.com/scikit-learn/scikit-learn
DBSCAN:https://github.com/scikit-learn/scikit-learn
這些是比較流行的監督學習模型,也是常用的機器學習算法,你可以根據你的需要來選擇相應的源碼。