Data Science Algorithms PDF

Data Science Algorithms PDF List of Algorithms in Data Science, AI (Artificial Intelligence) We described different types of AI algorithms in data science along with their categories in this article.

1. Linear Regression: We use Linear Regression algorithm to predict target column value, when the target column contains a large number of distinct values.

2. Logistic Regression: Used for classification-based problems, like yes-or-no questions, like whether an email is spam or not.

3. Decision Trees: Think of these as flowcharts that help in decision-making processes.

How Decision Trees Work: 1. Decision Nodes (Internal Nodes): At each internal node, the decision tree algorithm selects the best feature to split the data based on certain criteria. The goal is to find the feature that best separates the data into different classes or categories.

2. Branches: Based on the feature selected at a decision node, the data is split into subsets. Each branch represents a specific value or range for the chosen feature.

3. Leaf Nodes: The process continues recursively until a stopping criterion is met. This could be a maximum depth of the tree, a minimum number of samples in a leaf node, or other conditions. When the tree reaches a leaf node, it assigns a class label (in classification tasks) or a numerical value (in regression tasks) to the input data based on the majority class or the average value of the samples in that leaf node.

4. Random Forest: A bunch of decision trees working together, like a forest of solutions.

How Random Forest Works: Multiple Decision Trees A Random Forest consists of a collection of decision trees, where each tree is built using a subset of the training data and a random subset of features.

Bootstrap Aggregating (Bagging) ●

Random Forest employs a technique called bagging, where multiple decision trees are trained independently on different subsets of the training data.

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Each subset is created by sampling the training data with replacement (bootstrap sampling).

Random Feature Selection ●

For each split in a decision tree, Random Forest only considers a random subset of features instead of all features. This randomness introduces diversity among the trees.

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The number of features considered at each split is a hyperparameter that can be tuned.

Typically, the square root of the total number of features is a common choice.

Voting or Averaging ●

For classification tasks, the predictions from individual trees are combined through a majority voting mechanism. The class that receives the most votes from the trees becomes the final predicted class.

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For regression tasks, the predictions from individual trees are averaged to obtain the final regression output. This averaging helps in reducing the variance and improving the overall prediction.

Support Vector Machines (SVM): Draws a boundary between different types of data points.

Working Principle: 1. Feature Mapping: In data science SVM works well in high-dimensional spaces, so it can implicitly map input data points into a higher-dimensional space using a technique called the kernel trick. This allows SVM to find nonlinear decision boundaries in the original feature space.

2. Optimal Hyperplane: SVM finds the hyperplane that best separates the data points while maximizing the margin. The data points that are closest to the hyperplane are known as support vectors. These support vectors play a crucial role in determining the position and orientation of the hyperplane.

3. Handling Non-Linearity: When the data is not linearly separable in the original feature space, SVM uses various types of kernel functions (such as polynomial, radial basis function (RBF), or sigmoid kernels) to map the data into a higher-dimensional space where a linear hyperplane can effectively separate the classes. The choice of the kernel function significantly influences the performance of the SVM model.

4. Optimization Problem: SVM solves an optimization problem to find the optimal hyperplane.

The objective is to minimize the classification error (i.e., misclassified points) while maximizing the margin. Additionally, soft-margin SVM allows for some misclassification to handle noisy or overlapping data.

5. Kernel Trick: The kernel trick allows SVM to implicitly compute the higher-dimensional representation of the input data without explicitly transforming the data. This computational shortcut enables SVM to efficiently handle high-dimensional spaces.

Advantages of SVM: 1. Effective in High-Dimensional Spaces: SVM performs well even in high-dimensional spaces, making it suitable for problems with a large number of features.

2. Robust to Overfitting: SVM is less prone to overfitting, especially in high-dimensional spaces, due to the margin maximization objective.

3. Versatile Kernels: The ability to use different kernel functions allows SVM to handle complex, nonlinear relationships in the data.

4. Global Optimality: SVM aims to find the global optimum, ensuring a more reliable solution compared to some other algorithms.

5. Fewer Hyperparameters: SVM has relatively fewer hyperparameters to tune, making it easier to work with.

6. Naive Bayes: Figures out probabilities to make predictions, commonly used in spam filters.

7. K-Nearest Neighbors (KNN): Classifies things based on what their neighbors are like.

There are other algorithm categories also available like: 8. Neural Networks: A network of interconnected nodes inspired by the human brain, great for complex tasks.

9. Deep Learning Algorithms: Specialized neural networks for tasks like recognizing images or understanding language.

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