Section 1: Defining the Model — What is the Algorithm? K-Nearest Neighbors (KNN) is a supervised machine learning and lazy learning algorithm. KNNs purpose is to predict the classification of a query sample by relying on labeled input data which are separated into several classes. …

Section 1: Defining the Model — What is the Algorithm? Logistic Regression (LR) is a supervised machine learning algorithm. LR’s purpose is to predict the classification of a query sample (eg. yes/no). It predicts the probability (between 0 and 1) of the action using labeled input data with the help of a sigmoid function. …

Section 4: Evaluating the Model Tradeoffs — Reference How to Improve Naive Bayes? Section 3: Tuning the Model in Python, prior to continuing… A D V A N T A G E S Q1: Is Naive Bayes a simple or difficult classifier to understand? Answer: Simple Q2: Is Naive Bayes an interpretable classifier or not an interpretable classifier? Answer: Interpretable Q3: Is Naive Bayes a fast or…

Section 3: Tuning the Model in Python — Reference How to Implement Naive Bayes? Section 2: Building the Model in Python, prior to continuing… [10] Define Grid Search Parameters param_grid_nb = { 'var_smoothing': np.logspace(0,-9, num=100) } var_smoothing is a stability calculation to widen (or smooth) the curve and therefore account for more samples that are further away from…

Section 2: Building the Model in Python — Reference What is Naive Bayes? Section 1: Defining the Model, prior to continuing… [1] Import Libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd NumPy is a Python library used for working with arrays. Matplotlib is a Python library used for creating static, animated, and interactive…

Section 1: Defining the Model — What is the Algorithm? Naive Bayes (NB) is a supervised machine learning algorithm. NBs purpose is to predict the classification of a query sample by relying on labeled input data which are separated into classes. …

Section 4: Evaluating the Model Tradeoffs — Reference How to Improve Support Vector Machine? Section 3: Tuning the Model in Python, prior to continuing… A D V A N T A G E S Q1: Is Support Vector Machine a simple or difficult classifier to understand? Answer: Simple Q2: Can Support Vector Machine solve linear problems or non-linear problems? Answer: Linear Problems & Non-Linear Problems Q3: Does…

Section 3: Tuning the Model in Python — Reference How to Implement Support Vector Machine? Section 2: Building the Model in Python, prior to continuing… [10] Define Grid Search Parameters param_grid_svm = { 'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001], 'kernel': ['linear', 'rbf', 'poly', 'sigmoid'], 'class_weight': ['balanced'] }

Section 2: Building the Model in Python — Reference What is Support Vector Machine? Section 1: Defining the Model, prior to continuing… [1] Import Libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd NumPy is a Python library used for working with arrays. Matplotlib is a Python library used for creating static, animated, and…