Before this chapter, we discussed Bayesian learning.The Bayesian formula is as follows:P(y=c∣x)=P(x∣y=c)⋅P(y=c)In this formula:P(x∣y=c) is called the likelihoodP(y=c) is called the priorLet’s dive into an example to better understand this concept.Suppose we want to predict the class label for a given input feature vector.For example, imagine the input features are:x=[0.5, 0.3, 0.7, 0.8]When this..

According to the definition of conditional probability,P(X = x | Y = y) = p(X = x, Y = y) / p(Y = y).We can rewrite the numerator using the product rule:p(X = x, Y = y) = p(X = x) × p(Y = y | X = x).We can also rewrite the denominator using the law of total probability:p(Y = y) = Σ over x' [ p(X = x') × p(Y = y | X = x') ].For example, if the event Y = y corresponds to "ate melon",then the total..

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),..