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귀무 가설 H0 과 F-통계량: 귀무 가설 H0 은 회귀 모델에서 모든 독립 변수가 종속 변수에 영향을 미치지 않는다는 가설이다. 이를 검정하기 위해 F-통계량을 사용하며, 이 통계량은 모델의 설명 확률을 측정한다. F-분포: F-통계량은 정규 분포를 따르는 오차항을 가정할 때 F-분포를 따른다. F-분포는 두 개의 서로 다른 자유도를 가지며, 하나는 회귀에서 사용된 독립 변수의 수 p에 의해, 다른 하나는 표본 크기 n에서 독립 변수의 수를 뺀 값에 의해 결정된다. p-값 계산: 통계 소프트웨어는 주어진 F-통계량 값과 자유도를 사용하여 p-값을 계산한다. p-값은 특정한 F-통계량 값이 관측될 확률을 나타내며, 이 값이 매우 낮다면 (일반적으로 0.05 또는 0.01 미만) 우연히 발생했다고 보기 어..

Bias(편향) : Gap between the real problem and our model / The expected error created by using a model to apporoximate a real world function/relationship. 모델을 통해 얻은 예측값과 실제 정답과의 차이의 평균 f hat(x) : predicted value f(x) : true value Bias[^f(x)]=E[^f(x)−f(x)] Variance(분산) : change for different training data sets / The amount our predicted values would change if we had a differenct training dataset. It..

Criteria Logistic Regression Naive Bayes Classifier Linear Discriminant Analysis (LDA) Quadratic Discriminant Analysis (QDA) Model Type Parametric Parametric Parametric Parametric Assumption about Data Distribution None on distribution, assumes a linear relationship between log odds and features Assumes independence between features, with specific distribution per class Assumes Gaussian distribu..

Assessing Model Accuracy So many machine learning methods! • A single best method for all data sets? Nope! • One method may work best on a particular data set. • But, some other method may work better on a similar but different data set How to compare Methods? • Given a set of data, which method will produce the best result? • In other words, how to compare different learning methods? Measuring ..

Key terms Y = f(x1, x2, x3) - want to improve sales (Y) of a product -> Y: output variable, dependent variable -control adveritsing budgets : sns(x1), streaming(x2), flier(x3) ->x1, x2, x3 : input variables, independent variables, predictors Key questions 1) What is the relationship between x1, x2, x3 and Y? -> learning 2) How accurately can we predict Y from x1, x2, x3? -> prediction data -- (l..

statistical learning supervised/ unsupervised supervised : given data (x,y), build a model, a mapping Y(input) ~ f(x)(output) - use the model f to predict Y for unknown inputs X' find relationship between factors based on data ex) based on various values year, age, gender ... => wage consider outlier (이상치) https://en.wikipedia.org/wiki/Outlier Outlier - Wikipedia From Wikipedia, the free encyclo..

1. Input Layer:Purpose: It receives the input data and acts as a gateway to funnel the data into the network. Nodes: The number of nodes in the input layer typically matches the number of input features in the dataset. For instance, for an image that's 28x28 pixels (like those in the MNIST dataset), you'd have 784 input nodes.  2. Output Layer:Purpose: This is where the network provides its pred..

Limitations of the Simple Perceptron The primary limitation of a simple perceptron is its inability to handle data that isn't linearly separable. This means that if you can't draw a straight line (in two dimensions, or a hyper-plane in higher dimensions) to separate the classes, the perceptron won't be able to classify the data correctly. In the context of logical operations, a perceptron can mo..

Major Topics of NN NN or Neuron itself At its core, a neural network is comprised of nodes or "neurons", which are inspired by the biological neurons found in our brains. These artificial neurons receive inputs, process them (via weights and biases) and produce an output. Architectures FNN (Feedforward Neural Network): Data flows in one direction, from input to output. It consists of an input la..