Cheatsheets
Machine Learning: Supervised Learning

Machine Learning: Supervised Learning

Simple Linear Regression

Understanding Gradient Descent

Gradient descent helps find the best-fit line for your data. The 'learning rate' controls how quickly this line adjusts. If it's too high, the line might never settle correctly; if it's too low, it takes a long time to find the right line.

Applying Gradient Descent in Regression

Gradient descent is a method used to fine-tune the parameters in regression to minimize errors. It adjusts the parameters iteratively to reduce the difference between the predicted and actual values.

Classifying with K-Nearest Neighbors

Avoiding Overfitting and Underfitting in KNN

In K-Nearest Neighbors (KNN), choosing the right number of neighbors (k) is crucial. A small k can overfit the model to the training data, while a large k might underfit and not capture important patterns.

                                

from sklearn.neighbors import KNeighborsClassifier
KNNClassifier = KNeighborsClassifier(n_neighbors=5)
KNNClassifier.fit(X_train, y_train)
KNNClassifier.predict(X_test)

Using KNN in Scikit-Learn

Scikit-learn provides a KNeighborsClassifier that helps in classifying data using the KNN method. The 'n_neighbors' parameter defines how many closest neighbors are used to classify new data points.

                                

def distance(p1, p2):
  x_diff_squared = (p1[0] - p2[0]) ** 2
  y_diff_squared = (p1[1] - p2[1]) ** 2
  return (x_diff_squared + y_diff_squared) ** 0.5
distance( (0, 0), (3, 4) )      # => 5.0

Measuring Distance with Euclidean Method

Euclidean Distance is used to measure how far apart two points are. It's calculated as the square root of the sum of the squared differences between their coordinates.

                                

def distance(p1, p2):
  x_diff_squared = (p1[0] - p2[0]) ** 2
  y_diff_squared = (p1[1] - p2[1]) ** 2
  return (x_diff_squared + y_diff_squared) ** 0.5
distance( (0, 0), (3, 4) )      # => 5.0

Finding the Best k Value with the Elbow Method

The Elbow Method helps determine the optimal number of neighbors (k) in KNN. It plots the error rate for different k values and looks for the 'elbow' point where adding more neighbors doesn’t significantly reduce the error.

Basic Concept of K-Nearest Neighbors

In KNN, to find the closest data points, we use the distance formula, which is the square root of the sum of the squared differences between coordinates. This helps classify new data points based on their closest neighbors.

Predicting Unknown Data with KNN

To classify unknown data points, we calculate their distance from known data points and determine the majority class among the nearest neighbors.

Normalizing Data for Better KNN Results

Normalizing data ensures that all features contribute equally to the distance calculation in KNN, improving the model's accuracy.

K-Nearest Neighbor for Predictions

Using KNN for Regression Predictions

In KNN regression, predictions are made based on the average of the values from the nearest neighbors. It uses the similarity of features to predict new data values.

Logistic Regression Explained

How Logistic Regression Works in Scikit-Learn

Scikit-learn's Logistic Regression helps in classifying data into categories. It can adjust various settings, such as the type of penalty applied and the solver for optimization.

Using the Sigmoid Function in Logistic Regression

The sigmoid function in Logistic Regression converts predictions into probabilities, providing a value between 0 and 1 that indicates the likelihood of a particular class.

Understanding Classification Thresholds

A Classification Threshold is used to decide the cutoff point for classifying data. Typically set at 0.5, it can be adjusted for better accuracy based on the specific problem.

Interpreting Logistic Regression Results

Logistic Regression is easy to interpret because it provides clear coefficients that show how each feature affects the prediction outcome.

Calculating Log-Odds in Logistic Regression

Log-Odds represent the likelihood of an event occurring. In Logistic Regression, they are calculated by multiplying feature values by their coefficients.

Using Logistic Regression for Binary Classification

Logistic Regression is used for predicting binary outcomes, such as yes/no or true/false. It can also be extended to handle multiple classes by creating multiple binary classifiers.

Predicting with Logistic Regression

Logistic Regression predicts the probability of an event by creating a decision boundary that separates different classes based on the highest probability.

Measuring Accuracy with Log Loss

Log Loss, or Cross Entropy Loss, measures how well the Logistic Regression model's predictions match the actual outcomes. Lower values indicate better performance.

Understanding Decision Trees

Measuring Information Gain in Decision Trees

Information Gain measures how much a feature improves the classification by reducing uncertainty. It helps decide which feature to use for splitting data in decision trees.

Understanding Gini Impurity

Gini Impurity calculates how mixed the classes are in a dataset. A Gini Impurity of 0 means a pure dataset where all samples belong to the same class.

Creating Leaf Nodes in Decision Trees

Leaf nodes in decision trees are created when further splitting does not improve the classification. This is controlled by a minimum information gain threshold.

Building Optimal Decision Trees

Creating the best decision tree can be challenging. Simple methods may not always find the best solution, and various techniques are used to improve tree quality.

Understanding Decision Tree Structure

In a decision tree, leaves represent the final decisions or classifications, internal nodes represent features, and branches represent the possible values of these features.

Pruning Decision Trees

Pruning reduces the size of a decision tree to prevent overfitting. This involves removing branches that provide little additional value.

Constructing Decision Trees

Decision trees can become overly complex, leading to overfitting. Pruning helps simplify the tree, improving its ability to generalize to new data.

Introduction to Random Forests

Random Forests use multiple decision trees to improve classification accuracy. Each tree is built using a subset of the data and features.

Avoiding Overfitting with Random Forests

Random Forests help prevent overfitting by averaging the results of multiple trees, reducing the impact of individual overfitted trees.

Feature Selection in Random Forests

Random Forests use random subsets of features to build trees, improving generalization and reducing the risk of overfitting.

Improving Performance with Random Forests

Random Forests combine predictions from multiple trees to improve accuracy. This ensemble approach helps capture complex patterns in the data.

Bagging Technique in Random Forests

Bagging, or Bootstrap Aggregating, involves training multiple decision trees on different subsets of the data and combining their predictions to improve performance.

Naive Bayes for Classification

Understanding Statistical Dependence

Statistical Dependence refers to how features in a dataset influence each other. In Naive Bayes, it's assumed that features are independent, simplifying calculations.

Applying Naive Bayes in Text Classification

Naive Bayes is effective for text classification tasks, like spam detection, due to its ability to handle large feature sets efficiently.

Using Laplace Smoothing in Naive Bayes

Laplace Smoothing adjusts probabilities in Naive Bayes to handle zero-frequency problems, ensuring that even unseen features have a non-zero probability.

Understanding Naive Bayes Assumptions

Naive Bayes assumes that features are independent, which simplifies calculations. Despite this assumption, it often performs well in practice.

Calculating Probabilities with Naive Bayes

Naive Bayes uses Bayes' Theorem to calculate the probability of a class given the features. It multiplies the probabilities of each feature being in a class.

Predicting Outcomes with Naive Bayes

Naive Bayes predicts the class with the highest posterior probability, based on the features of the data.

Minimax for AI Decision Making

Exploring Minimax Algorithm for Game Strategy

The Minimax Algorithm is used in game theory to make optimal decisions by minimizing the possible loss for the worst-case scenario. It helps in strategic game planning.

Implementing Minimax for AI

In AI, Minimax helps in decision-making for games by considering the best possible moves for both players. It evaluates the game tree to choose the optimal strategy.

Enhancing Minimax with Alpha-Beta Pruning

Alpha-Beta Pruning improves the Minimax algorithm by eliminating branches that do not affect the final decision. This reduces the number of calculations needed.

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