Expert Solutions for Master-Level Machine Learning Assignments

Welcome to ProgrammingHomeworkHelp.com! If you're seeking expert assistance with your machine learning coursework, you're in the right place. Our team of professionals is here to help with your most challenging assignments. Whether you're grappling with complex algorithms or intricate data sets, our experts are equipped to handle it all. If you ever find yourself thinking, "Who can do my machine learning assignment?" look no further. In this post, we'll walk you through two advanced machine learning problems along with detailed solutions provided by our experts.

Question 1: Implementing a Custom K-Nearest Neighbors (KNN) Algorithm

Problem Statement:

You are required to implement a custom K-Nearest Neighbors (KNN) algorithm from scratch in Python. Your implementation should include the following functionalities:

Calculation of Euclidean distance between data points.
Classification based on majority voting among the nearest neighbors.
Handling both binary and multi-class classification.
Evaluating the accuracy of your classifier on a given test set.

Solution:

The K-Nearest Neighbors (KNN) algorithm is a fundamental classification method in machine learning. Below is a step-by-step implementation of a custom KNN algorithm:

import numpy as np
from collections import Counter
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

class CustomKNN:
def __init__(self, k=3):
self.k = k

def fit(self, X, y):
self.X_train = X
self.y_train = y

def euclidean_distance(self, x1, x2):
return np.sqrt(np.sum((x1 - x2)**2))

def predict(self, X):
y_pred = [self._predict(x) for x in X]
return np.array(y_pred)

def _predict(self, x):
distances = [self.euclidean_distance(x, x_train) for x_train in self.X_train]
k_indices = np.argsort(distances)[:self.k]
k_nearest_labels = [self.y_train[i] for i in k_indices]
most_common = Counter(k_nearest_labels).most_common(1)
return most_common[0][0]

def accuracy(self, y_true, y_pred):
return np.sum(y_true == y_pred) / len(y_true)

# Load a dataset
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the custom KNN
knn = CustomKNN(k=3)
knn.fit(X_train, y_train)
predictions = knn.predict(X_test)

# Evaluate the accuracy
accuracy = knn.accuracy(y_test, predictions)
print(f"Accuracy: {accuracy * 100:.2f}%")

In this implementation:

The CustomKNN class is defined with methods to fit the training data, predict the class of new data points, calculate Euclidean distances, and compute the classification accuracy.
The fit method stores the training data.
The euclidean_distance method computes the distance between two points.
The predict method classifies each test data point by majority vote from its nearest neighbors.
The accuracy method evaluates the model's performance on a test set.

Question 2: Building a Neural Network for Multi-Class Classification

Problem Statement:

Develop a neural network from scratch in Python to perform multi-class classification on the MNIST dataset. Your network should include:

An input layer.
At least one hidden layer with ReLU activation.
An output layer with softmax activation.
Backpropagation for training the network.
Evaluation of the model's accuracy on a test set.

Solution:

Building a neural network from scratch involves implementing forward and backward propagation, along with the necessary activation functions. Below is a basic implementation:

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils

class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.weights_input_hidden = np.random.randn(input_size, hidden_size) * 0.01
self.weights_hidden_output = np.random.randn(hidden_size, output_size) * 0.01
self.bias_hidden = np.zeros((1, hidden_size))
self.bias_output = np.zeros((1, output_size))

def relu(self, x):
return np.maximum(0, x)

def relu_derivative(self, x):
return np.where(x 0, 1, 0)

def softmax(self, x):
exp_x = np.exp(x - np.max(x, axis=1, keepdims=True))
return exp_x / np.sum(exp_x, axis=1, keepdims=True)

def forward(self, X):
self.hidden_input = np.dot(X, self.weights_input_hidden) + self.bias_hidden
self.hidden_output = self.relu(self.hidden_input)
self.final_input = np.dot(self.hidden_output, self.weights_hidden_output) + self.bias_output
self.final_output = self.softmax(self.final_input)
return self.final_output

def backward(self, X, y, learning_rate):
m = y.shape[0]
d_output = self.final_output - y
d_hidden_output = np.dot(d_output, self.weights_hidden_output.T) * self.relu_derivative(self.hidden_output)

self.weights_hidden_output -= learning_rate * np.dot(self.hidden_output.T, d_output) / m
self.bias_output -= learning_rate * np.sum(d_output, axis=0, keepdims=True) / m
self.weights_input_hidden -= learning_rate * np.dot(X.T, d_hidden_output) / m
self.bias_hidden -= learning_rate * np.sum(d_hidden_output, axis=0, keepdims=True) / m

def train(self, X, y, epochs, learning_rate):
for epoch in range(epochs):
self.forward(X)
self.backward(X, y, learning_rate)
if epoch % 100 == 0:
loss = -np.mean(y * np.log(self.final_output))
print(f'Epoch {epoch}, Loss: {loss}')

def predict(self, X):
output = self.forward(X)
return np.argmax(output, axis=1)

def accuracy(self, y_true, y_pred):
return np.mean(y_true == y_pred)

# Load and preprocess the MNIST dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], -1) / 255.0
X_test = X_test.reshape(X_test.shape[0], -1) / 255.0
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)

# Initialize and train the neural network
nn = NeuralNetwork(input_size=784, hidden_size=128, output_size=10)
nn.train(X_train, y_train, epochs=1000, learning_rate=0.01)

# Evaluate the model
predictions = nn.predict(X_test)
accuracy = nn.accuracy(np.argmax(y_test, axis=1), predictions)
print(f"Accuracy: {accuracy * 100:.2f}%")

In this implementation:

The NeuralNetwork class is defined with methods for the forward pass, activation functions, backpropagation, and training.
The relu and softmax functions serve as activation functions for the hidden and output layers, respectively.
The forward method computes the network's output given the input data.
The backward method updates the weights and biases based on the error gradients.
The train method iteratively updates the network's parameters to minimize the loss function.
The predict method returns the predicted class labels for the input data.
The accuracy method evaluates the model's performance.

These examples demonstrate the power of custom implementations in understanding the core mechanics of machine learning algorithms. By mastering these fundamental techniques, you can confidently tackle complex machine learning problems.

If you're facing any challenges with your machine learning assignments or need further assistance, don't hesitate to reach out to us at ProgrammingHomeworkHelp.com. Our team of experts is always ready to help you achieve academic excellence.