Let A be a linear transformation, where A ∈ R^(m×n).The set of all vectors x such that A x = 0 is called the null space of A.In geometric intuition, the null space represents the set of directions that get "squished" to the zero vector by A.For example, letA = [[1, 2], [3, 6]].In this case, the null space is{ [x, y]ᵗ ∈ R² | x = -2y },or equivalently,{ [-2y, y]ᵗ | y ∈ R }.One example from this sp..

#%%from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score, roc_curve,roc_auc_score, f1_score, precision_recall_curve from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.preprocessing import LabelEncoder import numpy as np import pandas as pd import matplotlib.pyplot as plt feature_name_df = pd.read_csv("data/har_dataset/features.txt..

import numpy as np# Classifier의 Decision Boundary를 시각화 하는 함수def visualize_boundary(model, X, y): fig,ax = plt.subplots() # 학습 데이타 scatter plot으로 나타내기 ax.scatter(X[:, 0], X[:, 1], c=y, s=25, cmap='rainbow', edgecolor='k', clim=(y.min(), y.max()), zorder=3) ax.axis('tight') ax.axis('off') xlim_start , xlim_end = ax.get_xlim() ylim_start , ylim_end = ax.get_yl..

Procedures are as follows: Introduction of confusion matrix, precision, recall, f1 score and roc_aucData PreprocessingData Splitting (Train/Test)Model Training and PredictionEvaluation (We will focus specifically on evaluation metrics.) 1. Introduction to Confusion Matrix, Precision, Recall, F1 Score, and ROC AUC→ Confusion MatrixThis is a matrix composed of four quadrants: False Negative (FN),..

Today, we are going to build a model using scikit-learn.Our workflow is as follows:Data Preprocessing :In this step, we will preprocess our dataset. For example, we will handle missing values, perform feature selection, and apply label encoding.Splitting the Data : Next, we will divide the data into training and test sets.Model Training : Here, we will train the model using the training data. We..

Data processing은 ML에서 algorithm만큼 중요하다고 한다. 그 이유는 결국 model이 학습하는 양분 자체가 data이기 때문이다. 이번 글에서는 Data를 어떻게 가공할 것이냐에 대해서 알아보고자 한다. 1. Data encoding 1.1 Label Encoding은 문자열(string) 또는 범주형(categorical) 데이터를 숫자형 category 값으로 변환하는 것을 말한다.가령, “냉장고”, “TV”, “에어컨”과 같은 제품명이 있다고 가정하자. 이러한 값들은 머신러닝 모델에 바로 사용할 수 없기 때문에, 숫자 형태로 변경해 주어야 한다. 이때 Label Encoding 과정을 이용할 수 있다. scikit-learn에는 LabelEncoder라는 클래스가 제공되어 ..