[Data Science] 앙상블(Ensemble) 1. 배깅(Bagging), 랜덤 포레스트(Random Forest)
Ensemble
Posts by Tag
- Data Analysis 9
- cs231 5
- image classification 5
- statistics 3
- R 3
- Ensemble 3
- time series 2
- MySQL 2
- Confusion Matrix 2
- Decision Tree 2
- DataScience 2
- Boosting 2
- kaggle 2
- matplotlib 2
- seaborn 2
- machineLearning 1
- Linear Regression 1
- Logisitc Regression 1
- Variable Selection 1
- Exhaustive Serach 1
- Forward Selection 1
- Backward Elimination 1
- Stepwise Selection 1
- Genetic algorithm 1
- K-Nearest Neighbor Learning 1
- K-Nearest Neighbor Regression 1
- K-Nearest Neighbor Classification 1
- Multiple Linear Regression 1
- Logistic Regression 1
- Kernel Based Learning 1
- Anomaly Detection 1
- Adaboost 1
- GBM 1
- XGBoost 1
- LightGBM 1
- sql 1
- haker rank 1
- coding test 1
Data Analysis
[Data Science] 2. 이상치 탐지(Density Based Anomaly Detection)
2. Density Based Anomaly Detection
[Data Science] 1. 커널 기반 학습(Kernel Based Learning)
1. Kernel Based Learning
[Data Analysis] 6. K-Nearest Neighbor Learning
Chapter 6. k-인접 이웃 기법
[Data Analysis] 5. Variable Selection
Chapter 5. 주요 변수 선택 기법
[Data Analysis] 4. Decision Tree
Chapter 4. 의사결정나무
[Data Analysis] 3. Confusion Matrix
Chapter 3. 분류 모형 성능 평가
[Data Analysis] 2. Logistic Regression
로지스틱 회귀분석 : Logisitc Linear Regression
[Data Analysis] 1. Multiple Linear Regression Analysis
다중 선형 회귀 모형 : Multivariate Linear Regression
cs231
[CS231N] 6. Training Neural Network 1
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
[CS231N] 5. Convolutional Neural Networks
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
[CS231N] 4. Intruduction to Neural Networks
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
[CS231N] 3. Loss Functions and Optimization
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
[CS231N] 2. Image Classification
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
image classification
[CS231N] 6. Training Neural Network 1
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
[CS231N] 5. Convolutional Neural Networks
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
[CS231N] 4. Intruduction to Neural Networks
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
[CS231N] 3. Loss Functions and Optimization
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
[CS231N] 2. Image Classification
💡 Iamge Classification Stanford University “CS231N” 강의를 듣고 정리하였습니다.
statistics
[Statistics] 확률변수
통계 정리
[Hands On ML] 한눈에 보는 머신러닝
💡 Hands On Machine Learning 오렐리앙 “핸즈온 머신러닝”을 보고 정리하였습니다.
[Time Series] 시계열 데이터
💡 시계열 데이터 분석 기초 유튜브 “곽기영” 강의를 보고 정리하였습니다.
R
[R] 3. Decision Tree with Confusion Matrix
Decision Tree Practice with R
[R] 2. Logistic Regression with Confusion Matrix
Logistic Regression Practice with R
[R] 1. Multiple Linear Regression
Multiple Linear Regression Practice with R
Ensemble
time series
[Statistics] 확률변수
통계 정리
[Time Series] 시계열 데이터
💡 시계열 데이터 분석 기초 유튜브 “곽기영” 강의를 보고 정리하였습니다.
MySQL
[MySQL] MySQL 정리 2
MySQL 내장함수
[MySQL] MySQL 정리 1
MySQL 소개
Confusion Matrix
[R] 2. Logistic Regression with Confusion Matrix
Logistic Regression Practice with R
[Data Analysis] 3. Confusion Matrix
Chapter 3. 분류 모형 성능 평가
Decision Tree
[R] 3. Decision Tree with Confusion Matrix
Decision Tree Practice with R
[Data Analysis] 4. Decision Tree
Chapter 4. 의사결정나무
DataScience
Boosting
kaggle
[Kaggle] Porti Seguro’s Safe Driver Prediction
Porti Seguro’s Safe Driver Prediction Data Description Porti Segure’s Sage Driver Prediction 유사한 그룹에 속하는 특징들은 특징 이름에 해당하는 태그로 표시됩니다 (예 : ind...
matplotlib
맨 위로 이동 ↑seaborn
맨 위로 이동 ↑machineLearning
[Hands On ML] 한눈에 보는 머신러닝
💡 Hands On Machine Learning 오렐리앙 “핸즈온 머신러닝”을 보고 정리하였습니다.
Linear Regression
[Data Analysis] 1. Multiple Linear Regression Analysis
다중 선형 회귀 모형 : Multivariate Linear Regression
Logisitc Regression
[Data Analysis] 2. Logistic Regression
로지스틱 회귀분석 : Logisitc Linear Regression
Variable Selection
[Data Analysis] 5. Variable Selection
Chapter 5. 주요 변수 선택 기법
Exhaustive Serach
[Data Analysis] 5. Variable Selection
Chapter 5. 주요 변수 선택 기법
Forward Selection
[Data Analysis] 5. Variable Selection
Chapter 5. 주요 변수 선택 기법
Backward Elimination
[Data Analysis] 5. Variable Selection
Chapter 5. 주요 변수 선택 기법
Stepwise Selection
[Data Analysis] 5. Variable Selection
Chapter 5. 주요 변수 선택 기법
Genetic algorithm
[Data Analysis] 5. Variable Selection
Chapter 5. 주요 변수 선택 기법
K-Nearest Neighbor Learning
[Data Analysis] 6. K-Nearest Neighbor Learning
Chapter 6. k-인접 이웃 기법
K-Nearest Neighbor Regression
[Data Analysis] 6. K-Nearest Neighbor Learning
Chapter 6. k-인접 이웃 기법
K-Nearest Neighbor Classification
[Data Analysis] 6. K-Nearest Neighbor Learning
Chapter 6. k-인접 이웃 기법
Multiple Linear Regression
[R] 1. Multiple Linear Regression
Multiple Linear Regression Practice with R
Logistic Regression
[R] 2. Logistic Regression with Confusion Matrix
Logistic Regression Practice with R
Kernel Based Learning
[Data Science] 1. 커널 기반 학습(Kernel Based Learning)
1. Kernel Based Learning
Anomaly Detection
[Data Science] 2. 이상치 탐지(Density Based Anomaly Detection)
2. Density Based Anomaly Detection