Nonparameteric Models

Advanced Machine Learning

     

Lecturer: Dr. HAS Sothea

🗺️ Content

• 1. Introduction
  

• 2. Theoretical Motivation
  

• 3. K-Nearest Neighbors
  

• 4. Decision Trees
  

• 5. Kernel Smoothing Methods
  

I. Introduction

I. Introduction

Supervised Learning Setting

Objective: Predicting target \(y\) using input \(x\).

• If \(y\) is continuous, the problem is called Regression.
• If \(y\) is discrete/categorical, it’s called Classification.

Criteria

• Quality of a prediction \(\hat{y}\) is measured by the loss: \(\ell(\hat{y},y)\).
• Quality of a model \(\hat{f}\) is measured by the risk: \[{\cal R}(\hat{f})=\mathbb{E}_{X,Y}[\ell(Y, \hat{f}(X))].\]

I. Introduction

Supervised Learning Setting

• Parametric models assume a specific form that depends on a fixed number of parameters. Example:
  • Linear model: \(\hat{f}_{\color{blue}{\beta}}(\text{x})=\color{blue}{\beta_0}+\sum_{j=1}^d\color{blue}{\beta_j}x_j\) with \(\color{blue}{\beta_0},\dots,\color{blue}{\beta_d}\in\mathbb{R}.\)
  • Polynomial model: \(\hat{f}_{\color{blue}{\beta}}(\text{x})=\color{blue}{\beta_0}+\sum_{p=0}^m\sum_{k=1}^d\sum_{j\leq k}\color{blue}{\beta_{jkp}}x_j^{p}x_k^{m-p}\) with \(\color{blue}{\beta_0},\color{blue}{\beta_{jkp}}\in\mathbb{R},\forall j,k,p.\)
  • \(2\)-layer Neural Networks: \(\hat{f}(\text{x})=\sigma_2\left(\color{blue}{W_2}\sigma_1(\color{blue}{W_1}x+\color{blue}{b_1})+\color{blue}{b_2}\right).\)
• Building a model is searching for \(\color{blue}{\beta^*}\) minimizing the risk, i.e., \[{\cal R}(\hat{f}_{\color{blue}{\beta^*}})=\inf_{\color{blue}{\beta}}\Big[\underbrace{{\cal R}(\hat{f}_{\color{blue}{\beta}})}_{\text{Goodness of fit}} + \underbrace{\color{gray}{\Omega(\color{blue}{\beta})}}_{\text{Flexibility}}\Big]\]

I. Introduction

Supervised Learning Setting

• Nonparametric models rely directly on the target \(y\) without assuming any form of input-output relationship.

• General weighted average form: \[\hat{f}(\text{x})=\sum_{i=1}^ny_i\color{blue}{W_{n,i}(\text{x})}.\]
• Interpretation:
  • The prediction of \(\text{x}\) is determined by averaging values of targets \(y_i\)’s.
  • Targets \(y_i\) that their input \(\text{x}_i\) is similar to \(\text{x}\) are more important and carry heavier weight \(\color{blue}{W_{n,i}(\text{x})}\) in the average.

🤔 Why does it work?

🗝️ Because it estimates \(\color{red}{\eta(x)=\mathbb{E}(Y|X=x)}\).

🤔 But what’s this \(\color{red}{\eta(x)}\)?

II. Theoretical Motivation

II. Theoretical Motivation

1. Regression theory: \(L_2\)/MSE minimizer

• \(\color{blue}{\text{Mean Squared Error}}\) of any model \(\hat{f}\) is defined by: \[\color{blue}{\text{MSE}}(\hat{f})=\mathbb{E}[(\hat{f}(X)-Y)^2].\]
• If \(\text{x}\) be an input and \(\color{red}{\eta(\text{x})=\mathbb{E}(Y|X=\text{x})}\) then \(\forall\hat{f}\): \[\begin{align*} \color{blue}{\text{MSE}}(\hat{f})&=\mathbb{E}[(\hat{f}(X)-\color{red}{\eta}(X))^2]+\mathbb{E}[(\color{red}{\eta}(X)-Y)^2] \end{align*}\]
• This implies that \(\color{red}{\eta}\) is the minimizer of \(\color{blue}{\text{MSE}}\) criterion, i.e., \[\color{blue}{\text{MSE}}(\color{red}{\eta})=\inf_{\hat{f}}\color{blue}{\text{MSE}}(\hat{f})=\mathbb{E}[(\color{red}{\eta}(X)-Y)^2].\]
• \(\color{red}{\eta}\) is called Regression Function/\(L_2\) Bayesian Estimator.

II. Theoretical Motivation

1. Regression theory: \(L_2\)/MSE minimizer

• \(\color{red}{\eta}\) is called Regression Function/\(L_2\) Bayesian Estimator.

• \(\color{red}{\eta(\text{x})=\mathbb{E}(Y|X=\text{x})}\) is the average of \(y\) for inputs that are similar to the query point \(\text{x}\). In discrete case: \[\begin{align*}\color{red}{\eta}(\text{x})&=\mathbb{E}(Y|X=\text{x})=\sum_{i=1}^{\infty}y_i\color{blue}{\mathbb{P}(Y=y_i|X=\text{x})} \end{align*}\]

• Recall that nonparam. models: \(\hat{f}(\text{x})=\sum_{i=1}^ny_i\color{blue}{W_{n,i}(\text{x})}.\)

II. Theoretical Motivation

1. Regression theory: \(L_2\)/MSE minimizer

• Nonparametric models: \[\hat{f}(\text{x})=\sum_{i=1}^ny_i\color{blue}{W_{n,i}(\text{x})}.\]

• Regression function: \[\color{red}{\eta}(\text{x})=\sum_{i=1}^{\infty}y_i\color{blue}{\mathbb{P}(Y=y_i|X=\text{x})}.\]

• Q1: Why don’t we compute \(\color{blue}{\mathbb{P}(Y=y_i|X=\text{x})}\) directly?

• A1: We don’t know the distribution of \(Y|X\).

• Weights \(\color{blue}{W_{n,i}(\text{x})}\) plays a role as an estimate of \(\color{blue}{\mathbb{P}(Y=y_i|X=\text{x})}\).

• Different choices of weights lead to different nonparametric models.

• Weights \(\color{blue}{W_{n,i}(\text{x})}\) are often defined according to how “close” \(\text{x}_i\) to \(\text{x}\).

• “Closeness” is often described by “metrics, neighborhoods or topological structures”…

II. Theoretical Motivation

2. Example: Missing Value Imputation

from skimage import data
import matplotlib.pyplot as plt
image = data.astronaut()
import numpy as np
from sklearn.impute import KNNImputer
n_miss = 100
np.random.seed(42)
missing_id = np.random.choice(512, size=(n_miss,2))
image_nan = image.copy()

for i in range(n_miss):
    image_nan[(missing_id[i,0]-2):(missing_id[i,0]+2),(missing_id[i,1]-2):(missing_id[i,1]+2),:] = 0
plt.figure(figsize=(4,4))
plt.imshow(image_nan)
plt.axis('off')
plt.show()

for i in range(n_miss):
    for ch in range(3):
        temp = image_nan[(missing_id[i,0]-5):(missing_id[i,0]+5), (missing_id[i,1]-5):(missing_id[i,1]+5), ch]
        image_nan[(missing_id[i,0]-2):(missing_id[i,0]+2),(missing_id[i,1]-2):(missing_id[i,1]+2),ch] = temp[temp> 0].mean()
plt.figure(figsize=(4,4))
plt.imshow(image_nan)
plt.axis('off')
plt.show()

• Missing pixels \(=\frac{1}{N}\sum_{k=1}^Np_k\) where \(p_k\) are neighboring non-missing pixels.
• Q2: What are the weights \(\color{blue}{W_{n,i}(\text{x})}\) in this case?

II. Theoretical Motivation

3. Decision theory for classification

• In classification, misclassification loss: \(\ell(\hat{f}(x),y)=\mathbb{1}_{\{\hat{f}(x)\neq y\}}=\begin{cases}1,\text{ if }\hat{f}(x)\neq y\\0,\text{ if }\hat{f}(x)=y\end{cases}\).
• The misclassification risk is given by: \[{\cal R}_c(\hat{f})=\mathbb{E}[\ell(\hat{f}(X),Y)]=\mathbb{E}[\mathbb{1}_{\{\hat{f}(X)\neq Y\}}]=\mathbb{P}(\hat{f}(X)\neq Y).\]
• This is equivalent to classification problems that maximize accuracy score because \[\text{Accuracy}=1-\mathbb{P}(\hat{f}(X)\neq Y)=\mathbb{P}(\hat{f}(X)=Y).\]

Lemma [Ch 2, Hastie et al. (2008) & Sec 1.4, Kyörfi et al. (2002)]

In balanced \(M\)-class classification, \[\text{If } g^*(x)=\arg\max_{1\leq k\leq M}\mathbb{P}(Y=k|X=x)\text{ then }{\cal R}_c(g^*)=\inf_{\hat{f}}{\cal R}_c(\hat{f}).\]

II. Theoretical Motivation

Summary

• In regression with MSE, \(\color{red}{\eta(\text{x})=\mathbb{E}(Y|X=\text{x})}\) is the best regressor.
  • We cannot compute it, but we can estimate it.
    • Parametric models assume that \(\color{red}{\eta}\) takes some forms (linear, polynomial…).
    • Nonparametric models attempt to estimate \(\color{red}{\eta}\) using weighted average form: \(\hat{f}(\text{x})=\sum_{i=1}^ny_i\color{blue}{W_{n,i}(x)}\).
• In balanced \(M\)-class classification with Accuracy score, \(g^*(\text{x})=\arg\max_{1\leq k\leq M}\color{blue}{\mathbb{P}(Y=k|X=\text{x})}\) is the best classifier.
• We will now look at three different models that attempt to estimate regression function \(\color{red}{\eta}\) using different form of weights \(\color{blue}{W_{n,i}(\text{x})}\).

III. \(K\)-Nearest Neighbors (\(K\)-NN)

III. \(K\)-Nearest Neighbors (\(K\)-NN)

1. Setting

• Given iid observations: \(\{(\text{x}_1,y_1),\dots, (\text{x}_n,y_n)\}\subset \mathbb{R}^d\times{\cal Y}\).
• If \(D\) is a distance on \(\mathbb{R}^d\) (e.g. Euclidean distance), the \(k\)-th nearest neighbor \(\color{red}{\text{x}_{(k)}}\) of \(\text{x}\in\mathbb{R}^d\) is defined by
  • \(D(\text{x},\text{x}_{(1)})\leq D(\text{x},\text{x}_{(2)})\leq\dots\leq D(\text{x},\color{red}{\text{x}_{(k)}})\leq \dots\leq D(\text{x},\text{x}_{(n)})\).
  • Let \(y_{(1)},\dots,y_{(n)}\) be the target of \(\text{x}_{(1)},\dots,\text{x}_{(n)}\) respectively.
• If \(K\geq 1\), then \(K\)-NN predicts the target of an input \(\text{x}\) by

• Regression: \[\begin{align*}\hat{y}&=\frac{1}{K}\sum_{k=1}^Ky_{(k)}\qquad\quad \color{gray}{(\color{blue}{W_{n,i}(\text{x})}=?)}\\ &=\text{Average $y_{(k)}$ among the $K$ neighbors}.\end{align*}\]

• Classification: \[\begin{align*}\hat{y}&=\arg\max_{1\leq m\leq M}\frac{1}{K}\sum_{k=1}^K\mathbb{1}_{\{y^{(k)}=m\}}\\ &=\text{Majority class among the $K$ neighbors.}\end{align*}\]

III. \(K\)-Nearest Neighbors (\(K\)-NN)

2. Example

• Regression: \[\begin{align*}\hat{y}&=\frac{1}{K}\sum_{k=1}^Ky_{(k)}\qquad\quad \color{gray}{(\color{blue}{W_{n,i}(\text{x})}=?)}\\ &=\text{Average $y_{(k)}$ among the $K$ neighbors}.\end{align*}\]

• Classification: \[\begin{align*}\hat{y}&=\arg\max_{1\leq m\leq M}\frac{1}{K}\sum_{k=1}^K\mathbb{1}_{\{y^{(k)}=m\}}\\ &=\text{Majority class among the $K$ neighbors.}\end{align*}\]

III. \(K\)-Nearest Neighbors (\(K\)-NN)

2. Example

• Let’s work with our Heart Disease Dataset (shpe: \(1025\times 14\)) and choose \(K=5\).
• \(K\)-NN is a distance-based method, it’s essential to
  • Scale the inputs
  • Watch out for outliers/missing values
  • Encode categorical inputs
  • Watch out for the effect of imbalanced class…

import numpy as np
import pandas as pd
data = pd.read_csv(path + "/heart.csv")
quan_vars = ['age','trestbps','chol','thalach','oldpeak']
qual_vars = ['sex','cp','fbs','restecg','exang','slope','ca','thal','target']

# Convert to correct types
for i in quan_vars:
  data[i] = data[i].astype('float')
for i in qual_vars:
  data[i] = data[i].astype('category')

# Train test split
from sklearn.model_selection import train_test_split
X, y = data.iloc[:,:-1], data.iloc[:,-1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)

from sklearn.preprocessing import MinMaxScaler, StandardScaler
scaler = MinMaxScaler()

# OnehotEncoding and Scaling
X_train_cat = pd.get_dummies(X_train.select_dtypes(include="category"), drop_first=True)
X_train_encoded = scaler.fit_transform(np.column_stack([X_train.select_dtypes(include="number").to_numpy(), X_train_cat]))
X_test_cat = pd.get_dummies(X_test.select_dtypes(include="category"), drop_first=True)
X_test_encoded = scaler.transform(np.column_stack([X_test.select_dtypes(include="number").to_numpy(), X_test_cat]))

# KNN
from sklearn.neighbors import KNeighborsClassifier 

knn = KNeighborsClassifier(n_neighbors=5)
knn = knn.fit(X_train_encoded, y_train)
y_pred = knn.predict(X_test_encoded)

from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay

test_perf = pd.DataFrame(
    data={'Accuracy': accuracy_score(y_test, y_pred),
          'Precision': precision_score(y_test, y_pred),
          'Recall': recall_score(y_test, y_pred),
          'F1-score': f1_score(y_test, y_pred),
          'AUC': roc_auc_score(y_test, y_pred)},
    columns=["Accuracy", "Precision", "Recall", "F1-score", "AUC"],
    index=["5NN"])
test_perf

	Accuracy	Precision	Recall	F1-score	AUC
5NN	0.868293	0.861111	0.885714	0.873239	0.867857

• Q3: Can we do better?
• A3: Yes! \(K=5\) is arbitrary. We should fine-tune it!

III. \(K\)-Nearest Neighbors (\(K\)-NN)

3. Influence of \(K\)

• Too small \(K\Leftrightarrow\) too flexible \(\Leftrightarrow\) high variance \(\Leftrightarrow\) Overfitting.

III. \(K\)-Nearest Neighbors (\(K\)-NN)

4. Fine-tuning \(K\): Cross-validation

• There are many way to perform Cross-validation in python.
• Let’s use GridSearchCV from sklearn.model_selection module.

knn = KNeighborsClassifier()
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import f1_score, make_scorer
scorer = make_scorer(f1_score, pos_label=1)
param_grid = {'n_neighbors': list(range(2,31))}
knn = KNeighborsClassifier()
grid_search = GridSearchCV(knn, param_grid, cv=10, scoring=scorer, return_train_score=True)
grid_search.fit(X_train_encoded, y_train)

knn = KNeighborsClassifier(n_neighbors=grid_search.best_params_['n_neighbors'])
knn = knn.fit(X_train_encoded, y_train)
y_pred = knn.predict(X_test_encoded)

test_perf = pd.concat([test_perf, pd.DataFrame(
    data={'Accuracy': accuracy_score(y_test, y_pred),
          'Precision': precision_score(y_test, y_pred),
          'Recall': recall_score(y_test, y_pred),
          'F1-score': f1_score(y_test, y_pred),
          'AUC': roc_auc_score(y_test, y_pred)},
    columns=["Accuracy", "Precision", "Recall", "F1-score", "AUC"],
    index=[f"{grid_search.best_params_['n_neighbors']}NN"])], axis=0)
test_perf

	Accuracy	Precision	Recall	F1-score	AUC
5NN	0.868293	0.861111	0.885714	0.873239	0.867857
2NN	0.970732	1.000000	0.942857	0.970588	0.971429

III. \(K\)-Nearest Neighbors (\(K\)-NN)

4. Fine-tuning \(K\): Cross-validation

• What can go wrong?

• Duplicated data!
• Let’s try again.

	Accuracy	Precision	Recall	F1-score	AUC
5NN	0.868293	0.861111	0.885714	0.873239	0.867857
2NN	0.970732	1.000000	0.942857	0.970588	0.971429
16NN_No_Dup	0.885246	0.882353	0.909091	0.895522	0.883117

III. \(K\)-Nearest Neighbors (\(K\)-NN)

5. Curse of dimensionality

• Curse of dimensionality refers to various challenges and phenomena that arise when working with high-dimensional data.
• The main challenge for \(K\)-NN is that distances or closeness lose its meaning in high-dimensional spaces.
• In this scenario, data points tend to be equally distant from one another.
• Example: Simulate \(\text{x}_1,\dots,\text{x}_n\sim{\cal U}[-5,5]^d\) with \(d=1,10,100,500,1000, 5000, 10000, 50000\).
  • For each dimension \(d\), we compute: \[r(d)=\frac{\max_{i\neq j}D(\text{x}_i,\text{x}_j)}{\min_{i\neq j}D(\text{x}_i,\text{x}_j)}.\]
  • Obtain the following graph 👉

N = 10
Ds = np.zeros(shape=(N*(N-1)//2, 8))
j = 0
for d in [1,10,100, 500, 1000, 5000, 10000, 50000]:
    Xd = np.random.uniform(-5,5,size=(N,d))
    i = 0
    for s in range(1, N):
        for k in range(s):
            Ds[i,j] = np.linalg.norm(Xd[s,:] - Xd[k,:])
            i += 1
    j += 1
import plotly.express as px
df_dist = pd.DataFrame({'Ratio': np.round(Ds.max(axis=0)/Ds.min(axis=0), 2), 'Dim': ['1','10','100', '500', '1000', '5000', '10000', '50000']})
fig4 = px.line(df_dist, x='Dim', y="Ratio", text="Ratio")
fig4.update_layout(
    width=400, height=450, 
    title="Max-min distance ratio at various dimension")
fig4.update_xaxes(title='Dimension')
fig4.update_yaxes(title='Max-Min Distance Ratio')
fig4.update_traces(textposition="top right")
fig4.show()

III. \(K\)-Nearest Neighbors (\(K\)-NN)

Summary

• \(K\)-NN is a nonparametric model with weights: \(\color{blue}{W_{n,i}(\text{x})=\frac{\mathbb{1}_{\{\text{x}_i\in{\cal N}_{\text{x}}\}}}{K}}\), where \({\cal N}_{\text{x}}=\{\text{x}_{(1)},\dots,\text{x}_{(K)}\}\) the set of the first \(K\)-nearest neighbors of \(\text{x}\).

• Data preprocessing is essential: scaling, encoding, outliers…

• The key parameter \(K\) can be tuned using cross-validation technique.

• \(K\)-NN may not be suitable in high-dimensional cases due to Curse of dimensionality. However, we can try:

  • Feature selection
  • Dimensional reduction
  • Distance metric

III. \(K\)-Nearest Neighbors (\(K\)-NN)

\(K\)-NN on Mnist

• We try some different approaches:
  • Normal \(3\)NN (various sources, for example, see here)
  • Dimensional reduction with PCA with cross-validation
  • Fourier transform method with cross-validation.

from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# Reshape data from 3D array to (n, 784)
X_train_reshaped = X_train.reshape(-1, 28*28)/255.0
X_test_reshaped = X_test.reshape(-1, 28*28)/255.0

# Model
knn = KNeighborsClassifier(n_neighbors=3)
knn = knn.fit(X_train_reshaped, y_train)
y_pred = knn.predict(X_test_reshaped)

test_mnist = pd.DataFrame(
    data={'Accuracy': accuracy_score(y_test, y_pred),
          'Precision': precision_score(y_test, y_pred, average="macro"),
          'Recall': recall_score(y_test, y_pred, average="macro"),
          'F1-score': f1_score(y_test, y_pred, average="macro")},
    columns=["Accuracy", "Precision", "Recall", "F1-score"],
    index=[f"3NN"])

# PCA
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
pca = PCA(n_components=150)
X_proj_train = pca.fit_transform(X_train_reshaped)
X_proj_test = pca.transform(X_test_reshaped)

# Model
param_grid = {'n_neighbors': [1,2,3]}#list(range(2,31))}
knn = KNeighborsClassifier()
grid_search = GridSearchCV(knn, param_grid, cv=5, scoring=scorer, return_train_score=True)
grid_search.fit(X_proj_train, y_train)

knn = KNeighborsClassifier(n_neighbors=grid_search.best_params_['n_neighbors'])
knn = knn.fit(X_proj_train, y_train)
y_pred = knn.predict(X_proj_test)

test_mnist = pd.concat([test_mnist, pd.DataFrame(
    data={'Accuracy': accuracy_score(y_test, y_pred),
          'Precision': precision_score(y_test, y_pred, average="macro"),
          'Recall': recall_score(y_test, y_pred, average="macro"),
          'F1-score': f1_score(y_test, y_pred, average="macro")},
    columns=["Accuracy", "Precision", "Recall", "F1-score"],
    index=[f"{grid_search.best_params_['n_neighbors']}NN_PCA"])], axis=0)

# Fourier transform
from scipy.fftpack import fft2, fftshift
def compute_fourier_transform(image): 
    f_transform = fft2(image) 
    f_transform_shifted = fftshift(f_transform) 
    magnitude_spectrum = np.abs(f_transform_shifted) 
    return magnitude_spectrum

X_train_fft = np.array([compute_fourier_transform(img) for img in X_train]).reshape(-1, 28*28)
X_test_fft = np.array([compute_fourier_transform(img) for img in X_test]).reshape(-1, 28*28)

# Model
knn = KNeighborsClassifier()
grid_search = GridSearchCV(knn, param_grid, cv=5, scoring=scorer, return_train_score=True)
grid_search.fit(X_train_fft, y_train)

knn = KNeighborsClassifier(n_neighbors=grid_search.best_params_['n_neighbors'])
knn = knn.fit(X_train_fft, y_train)
y_pred = knn.predict(X_test_fft)

test_mnist = pd.concat([test_mnist, pd.DataFrame(
    data={'Accuracy': accuracy_score(y_test, y_pred),
          'Precision': precision_score(y_test, y_pred, average="macro"),
          'Recall': recall_score(y_test, y_pred, average="macro"),
          'F1-score': f1_score(y_test, y_pred, average="macro")},
    columns=["Accuracy", "Precision", "Recall", "F1-score"],
    index=[f"{grid_search.best_params_['n_neighbors']}NN_FT"])], axis=0)
test_mnist

	Accuracy	Precision	Recall	F1-score
3NN	0.9705	0.970912	0.970114	0.970375
1NN_PCA	0.9697	0.969680	0.969413	0.969492
1NN_FT	0.9271	0.927339	0.926162	0.926442

IV. Decision Trees

IV. Decision Trees 🌲

1. CART: Classification And Regression Trees

• In CART, “neighbors” are defined by rectangular regions within inputs space.

• Neighbors in \(k\)-NN are based on straight distance.

• Neighbors in CART are based on blocks.

IV. Decision Trees 🌲

1. CART: Classification And Regression Trees

• Building a CART consists of:
  • Start at root (no split yet).
  • Recursively split into smaller regions.
  • Stop when a stopping criterion is met.
• Regions \(\color{blue}{\Rightarrow}\) neighbors \(\color{blue}{\Rightarrow}\) prediction.

• At each split,
  • We try column \(\color{red}{X_j}\) at threshold \(\color{red}{a}\in\mathbb{R}\) into two subregions \(R_1\) and \(R_2\).
  • We decision to split along \(\color{red}{X_j}\) at \(\color{red}{a}\) so that \(R_1\) and \(R_2\) are as pure as possible.
• Impurity is defined by impurity measures:
  • Regression: Within-region variation \(\sum_{y\in R_1}(y-\overline{y}_1)^2+\sum_{y\in R_2}(y-\overline{y}_2)^2.\)
  • Classification (\(M\) classes):
    • Missclassification error \(=1-\hat{p}_{k^*}\) where \(k^*\) is the majority class.
    • Gini impurity \(=\sum_{k=1}^M\hat{p}_{k}(1-\hat{p}_{k})\).
    • Entropy \(=-\sum_{k}\hat{p}_{k}\log(\hat{p}_{k})\) where \(\hat{p}_{k}\): proportion of class \(k\) in region \(R\).

IV. Decision Trees 🌲

1. CART: Classification And Regression Trees

• Building a CART consists of:
  • Start at root (no split yet).
  • Recursively split into smaller regions.
  • Stop when a stopping criterion is met.
• Regions \(\color{blue}{\Rightarrow}\) neighbors \(\color{blue}{\Rightarrow}\) prediction.

• At each split,
  • We try column \(\color{red}{X_j}\) at threshold \(\color{red}{a}\in\mathbb{R}\) into two subregions \(R_1\) and \(R_2\).
  • We decision to split along \(\color{red}{X_j}\) at \(\color{red}{a}\) so that \(R_1\) and \(R_2\) are as pure as possible.
• Impurity is defined by impurity measures:

The smaller \(\Leftrightarrow\) the purer the regions!

IV. Decision Trees 🌲

1. CART: Classification And Regression Trees

• First split:
  • \(\text{En}(R_1)=-1\log(1)=0\)
  • \(\begin{align*}\\ \text{En}(R_2)&=-\color{blue}{16/19\log(16/19)}-\color{red}{3/19\log(3/19)}\\ &=0.436.\end{align*}\)
  • \(\text{En}_1=(0)11/30+(0.436)19/30=0.276.\)
  • Information gain: \(\text{En}_0-\text{En}_1.\)

• Prediction rule:
  • Regression: \(\color{blue}{\hat{y}}=\) average targets within the same block.
  • Classification: \(\color{blue}{\hat{y}}=\) majority vote among points within the same block.

IV. Decision Trees 🌲

1. CART: Classification And Regression Trees

• Hyperparameters:
  • max_depth
  • max_features
  • min_samples_split
  • min_samples_leaf
  • criterion… [see explanation here]

• Deep trees \(\Leftrightarrow\) less neighbors \(\Rightarrow\) Overfitting.
• In this case of smaller leaves, it’s similar to smaller \(k\) in \(k\)-NN.
• These hyperparameters should be fine-tuned using CV to optimize its performance.

IV. Decision Trees 🌲

2. Pruning Tree

• Hyperparameters:
  • max_depth
  • max_features
  • min_samples_split
  • min_samples_leaf
  • criterion… [see explanation here]

• Pruning criterion to be minimized: \(C_{\alpha}(T)=\sum_{j=1}^{|T|}N_j\text{Imp}_j(T)+\alpha|T|\) where
  • \(|T|\): num. of leaves of the tree \(T\) and
  • \(\text{Imp}_j(T)\): the impurity at leave \(j\)-th.

IV. Decision Trees 🌲

3. Tree Playground

IV. Decision Trees 🌲

3. Application: Heart Disease Dataset

• We use GridsearchCV to search over the hyperparameters:
  • Impurity (criterion)
  • Depth (max_depth)
  • Mininum size of leave nodes (min_samples_leaf)
  • Maximum features (max_features).

data = pd.read_csv(path + "/heart.csv")
quan_vars = ['age','trestbps','chol','thalach','oldpeak']
qual_vars = ['sex','cp','fbs','restecg','exang','slope','ca','thal','target']

# Convert to correct types
for i in quan_vars:
  data[i] = data[i].astype('float')
for i in qual_vars:
  data[i] = data[i].astype('category')

data = data.drop_duplicates()
# Train test split
from sklearn.model_selection import train_test_split
X, y = data.iloc[:,:-1], data.iloc[:,-1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)

from sklearn.model_selection import GridSearchCV 
from sklearn.tree import DecisionTreeClassifier 
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay

clf = DecisionTreeClassifier()
param_grid = {'criterion': ['gini', 'entropy'], 
              'max_depth': [10, 20, 30, 40, 50], 
              'min_samples_split': [2, 5, 10], 
              'min_samples_leaf': [1, 2, 4], 
              'max_features': ['auto', 'sqrt', 'log2'] }
grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1) 
grid_search.fit(X_train, y_train)

best_model = grid_search.best_estimator_ 
y_pred = best_model.predict(X_test)

test_tr = pd.DataFrame(
    data={'Accuracy': accuracy_score(y_test, y_pred),
          'Precision': precision_score(y_test, y_pred),
          'Recall': recall_score(y_test, y_pred),
          'F1-score': f1_score(y_test, y_pred),
          'AUC': roc_auc_score(y_test, y_pred)},
    columns=["Accuracy", "Precision", "Recall", "F1-score", "AUC"],
    index=["Tree"])
test_tr

	Accuracy	Precision	Recall	F1-score	AUC
Tree	0.622951	0.727273	0.484848	0.581818	0.635281

IV. Decision Trees 🌲

Summary

• CART is a nonparametric model with weights: \(\color{blue}{W_{n,i}(\text{x})=\frac{\mathbb{1}_{\{\text{x}_i\in R_{\text{x}}\}}}{|R_{\text{x}}|}}\), where \(R_{\text{x}}\) the is the leave node containing \(\text{x}\).

• They are not sensitive to scaling.
• The key parameters includes
  • depth, minimum leave size,
  • impurity measures, number of splits,
  • maximum features considered at each split…
• They should be fine-tuned to optimize the model performance.
• It can handle categorical data as well.
• Just ike \(K\)-NN with small \(K\), deep trees are prone to overfitting.
• They should be pruned.

V. Kernel Smoothing Methods

V. Kernel Smoothing Methods

1. Smoother weights

• This is inspired by density estimation.
• It aims at directly estimating \(\eta(x)=\mathbb{E}(Y|X=x)\) for regression or \(\mathbb{P}(Y=k|X=x)\) in classifications.

• Regression: \[\hat{y}=\sum_{i=1}^ny_i\color{blue}{W_{n,i}(\text{x})},\] where \(\color{blue}{W_{n,i}(\text{x})}=\frac{K_h(\|\text{x}_i-\text{x}\|)}{\sum_{j=1}^nK_h(\|\text{x}_j-\text{x}\|)}\) with \(K_h(t)=K(t/h)\) for some moothing parameter \(h>0\).

• Classification: \[\hat{y}=\arg\max_{1\leq k\leq M}\sum_{i=1}^n\mathbb{1}_{\{y_i=k\}}\color{blue}{W_{n,i}(\text{x})}.\]
• A common ex. Gaussian Kernel \(K(t)=\exp(-t^2/2\sigma^2)\) for some \(\sigma>0\).

V. Kernel Smoothing Methods

1. Smoother weights

1.1. Example: Gaussian Kernel

• Building a kernel smoother \(\Leftrightarrow\) tuning \(h>0\) (cross-validation).

V. Kernel Smoothing Methods

1. Smoother weights

1.2. Example: Auto-MPG Dataset

	mpg	cylinders	displacement	horsepower	weight	acceleration	model year	origin
mpg	1.000000	-0.777618	-0.805127	-0.778427	-0.832244	0.423329	0.580541	0.565209
cylinders	-0.777618	1.000000	0.950823	0.842983	0.897527	-0.504683	-0.345647	-0.568932
displacement	-0.805127	0.950823	1.000000	0.897257	0.932994	-0.543800	-0.369855	-0.614535
horsepower	-0.778427	0.842983	0.897257	1.000000	0.864538	-0.689196	-0.416361	-0.455171
weight	-0.832244	0.897527	0.932994	0.864538	1.000000	-0.416839	-0.309120	-0.585005
acceleration	0.423329	-0.504683	-0.543800	-0.689196	-0.416839	1.000000	0.290316	0.212746
model year	0.580541	-0.345647	-0.369855	-0.416361	-0.309120	0.290316	1.000000	0.181528
origin	0.565209	-0.568932	-0.614535	-0.455171	-0.585005	0.212746	0.181528	1.000000

V. Kernel Smoothing Methods

2. Application: Auto-MPG Dataset

X = X.drop_duplicates()
X_train, X_test, y_train, y_test = train_test_split(X.iloc[:,1:], X.iloc[:,0], test_size=0.2, random_state=42)
from sklearn.neighbors import KNeighborsRegressor 
from sklearn.tree import DecisionTreeRegressor 
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.preprocessing import MinMaxScaler, StandardScaler
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.fit_transform(X_test)

# Define the KNN and Decision Tree Regressor 
knn = KNeighborsRegressor()
param_grid = {'n_neighbors': [2, 3, 5, 7, 9, 11, 13, 15, 17, 20]}
grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=-1) 
# Fit the model
grid_search.fit(X_train_scaled, y_train) 

# Extract the best model and parameters 
knn = KNeighborsRegressor(n_neighbors = grid_search.best_params_['n_neighbors'])
knn = knn.fit(X_train_scaled, y_train)
y_pred = knn.predict(X_test_scaled)
df_mse = pd.DataFrame(
    {'RMSE': np.round(np.sqrt(mean_squared_error(y_test, y_pred)), 3),
    'MAE': np.round(mean_absolute_error(y_test, y_pred), 3)},
    index = [f"{grid_search.best_params_['n_neighbors']}NN"]
)

tree = DecisionTreeRegressor() 
param_grid = {'criterion': ['squared_error', 'absolute_error'], 
              'min_samples_leaf': [2, 3, 5, 7, 10],
              'max_depth' : [5, 10, 15, 20, 30]}
grid_search = GridSearchCV(tree, param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=-1) 
# Fit the model
grid_search.fit(X_train, y_train)
best_params = grid_search.best_params_
tree = DecisionTreeRegressor(**best_params)
tree = tree.fit(X_train, y_train)
y_pred = tree.predict(X_test)

df_mse = pd.concat([df_mse, pd.DataFrame(
    {'RMSE': np.round(np.sqrt(mean_squared_error(y_test, y_pred)), 3),
    'MAE': np.round(mean_absolute_error(y_test, y_pred), 3)},
    index = [f"Tree"])])

# Kernel smoother
from gradientcobra.gradientcobra import KernelSmoother
ks = KernelSmoother(opt_method='grid',
                    bandwidth_list = np.linspace(0.00001, 50, 300),
                    show_progress = False)
ks = ks.fit(X_train_scaled, y_train)
y_pred = ks.predict(X_test_scaled)

df_mse = pd.concat([df_mse, pd.DataFrame(
    {'RMSE': np.round(np.sqrt(mean_squared_error(y_test, y_pred)), 3),
    'MAE': np.round(mean_absolute_error(y_test, y_pred), 3)},
    index = [f"Kernel Smoother"])])
df_mse

	RMSE	MAE
3NN	2.624	1.822
Tree	2.741	2.087
Kernel Smoother	2.460	1.783

V. Kernel Smoothing Methods

Summary

• Kernel smoother is a nonparametric model that assigns weight to each points using some kernel function \(K\), i.e., \[W_{n,i}(\text{x})=\frac{K_h(\|\text{x}_i-\text{x}\|)}{\sum_{j=1}^nK_h(\|\text{x}_j-\text{x}\|)}.\]
• Training a Kernel Smoother is equivalent to tuning the smoothing parameter \(h>0\) using, for example, cross validation error.
• Preprocessing is essential: scaling, encoding, transformation,…
• Just like in \(K\)-NN, it might not work well in high-dimensional case but can be improved using the same methods.

Nonparametric Methods (Summary)

Pros

• Flexibility: Do not assume specific form of model.
• No Distribution Assumptions: Do not require assumptions about the data distribution.
• Performance on Large Datasets: Decision Trees can handle high dimensional and large datasets.
• Model Complexity: Its complexity grows with data complexity. Less parameter to tune.

Cons

• Computationally Intensive: Can be computationally expensive, often require memory and processing power.
• Overfitting: Due to its flexibility, it might overfit the data without careful fine-tuning.
• Interpretability: Mostly not interpretable except for decision trees.
• Key Parameter Tuning: It requires proper key parameter tuning.

🥳 It’s party time 🥂