Machine Learning Algorithm - Logistic Regression
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Logistic regression is a classification algorithm used to predict binary outcomes. It transforms predictor variable values using the sigmoid function to produce a probability value between 0 and 1. The log odds of the outcome are modeled as a linear combination of the predictor variables. Positive coefficient values increase the probability of the outcome while negative values decrease the probability. Logistic regression outputs probabilities that can be converted into binary class predictions.
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![Sigmoid Function Refresher
The logistic function or a Sigmoid function is a function which takes any real value between zero and one. It is
defined as
y = 1 / (1 + e-x)
where y is the scaled value and x is the input value.
The logistic function transforms the original range of [-,] to [0,1] and also has a linear part on the transform.](https://image.slidesharecdn.com/lesson15ml-logisticregression-200627082721/85/Machine-Learning-Algorithm-Logistic-Regression-3-320.jpg)
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Clustering - Machine Learning TechniquesKush Kulshrestha This document provides an overview of unsupervised learning techniques, specifically clustering algorithms. It discusses the differences between supervised and unsupervised learning, the goal of clustering to group similar observations, and provides examples of K-Means and hierarchical clustering. For K-Means clustering, it outlines the basic steps of randomly assigning clusters, calculating centroids, and repeatedly reassigning points until clusters stabilize. It also discusses selecting the optimal number of clusters K and presents pros and cons of clustering techniques.
Machine Learning Algorithm - KNN
Machine Learning Algorithm - KNNKush Kulshrestha The k-nearest neighbors (kNN) algorithm assumes similar data points exist in close proximity. It calculates the distance between data points to determine the k nearest neighbors, where k is a user-defined value. To classify a new data point, kNN finds its k nearest neighbors and assigns the most common label from those neighbors. Choosing the right k value involves testing different k and selecting the one that minimizes errors while maintaining predictive accuracy on unseen data to avoid underfitting or overfitting. While simple to implement, kNN performance degrades with large datasets due to increased computational requirements.
Machine Learning Algorithm - Decision Trees
Machine Learning Algorithm - Decision Trees Kush Kulshrestha The document discusses decision trees as a key classification technique in machine learning, highlighting their structure, advantages, and common terminology. It explains the algorithm's process for creating a tree by recursively selecting attributes to split data based on measures like information gain and Gini index, which assess the purity of data subsets. Constraints on tree size are also addressed, including minimum samples for node splits, maximum depth, and maximum terminal nodes, essential for managing overfitting.
Machine Learning Algorithm - Naive Bayes for Classification
Machine Learning Algorithm - Naive Bayes for ClassificationKush Kulshrestha The document discusses Naive Bayes classification. It begins by explaining Bayes' theorem and how prior probabilities can impact classification. It then provides a candy selection example to introduce Naive Bayes, noting how assuming independence between variables (even if they are dependent) simplifies calculations. The document explains how Naive Bayes works using weather data, shows an example calculation, and lists pros and cons of the technique, such as its simplicity but limitation of assuming independence.
Assumptions of Linear Regression - Machine Learning
Assumptions of Linear Regression - Machine LearningKush Kulshrestha There are 5 key assumptions in linear regression analysis:
1. There must be a linear relationship between the dependent and independent variables.
2. The error terms cannot be correlated with each other.
3. The independent variables cannot be highly correlated with each other.
4. The error terms must have constant variance (homoscedasticity).
5. The error terms must be normally distributed. Violations of these assumptions can result in poor model fit or inaccurate predictions. Various tests can be used to check for violations.
Interpreting Regression Results - Machine Learning
Interpreting Regression Results - Machine LearningKush Kulshrestha The document discusses how to interpret the results of linear regression analysis. It explains that p-values indicate whether predictors significantly contribute to the model, with lower p-values meaning a predictor is meaningful. Coefficients represent the change in the response variable per unit change in a predictor. The constant/intercept is meaningless if all predictors cannot realistically be zero. Goodness-of-fit is assessed using residual plots and R-squared, though high R-squared does not guarantee a good fit. Interpretation focuses on statistically significant predictors and coefficients rather than fit metrics alone.
Machine Learning Algorithm - Linear Regression
Machine Learning Algorithm - Linear RegressionKush Kulshrestha This document provides an overview of linear regression machine learning techniques. It introduces linear regression models using one feature and multiple features. It discusses estimating regression coefficients to minimize error and find the best fitting line. The document also covers correlation, explaining that a correlation does not necessarily indicate causation. Multiple linear regression is described as fitting a linear function to multiple predictor variables. The risks of overfitting with too complex a model are noted. Code examples of implementing linear regression in Scikit-Learn and Statsmodels are referenced.
General Concepts of Machine Learning
General Concepts of Machine LearningKush Kulshrestha The document provides an overview of machine learning concepts including supervised and unsupervised learning algorithms. It discusses splitting data into training and test sets, training algorithms on the training set, testing algorithms on the test set, and measuring performance. For supervised learning, it describes classification and regression tasks, the bias-variance tradeoff, and how supervised algorithms learn by minimizing a loss function. For unsupervised learning, it discusses clustering, representation learning, dimensionality reduction, and exploratory analysis use cases.
Performance Metrics for Machine Learning Algorithms
Performance Metrics for Machine Learning AlgorithmsKush Kulshrestha This document discusses various performance metrics used to evaluate machine learning models, particularly for classification problems, including confusion matrix, accuracy, precision, recall, specificity, F1 score, log loss, and area under the curve (AUC). It emphasizes the importance of selecting appropriate metrics based on the context of the problem, detailing scenarios where minimizing false positives or false negatives is crucial. Additionally, it provides mathematical formulations and practical examples to illustrate the application and significance of these metrics.
Visualization-2
Visualization-2Kush Kulshrestha The document discusses the importance of data visualization, highlighting how different datasets that appear similar through summary statistics can reveal distinct stories when graphed. It covers concepts of cognitive load, clutter in visualization, and Gestalt principles that enhance the clarity and effectiveness of visual communication. Additionally, it emphasizes the need to minimize unnecessary complexity to improve understanding and retention of information.
Visualization-1
Visualization-1Kush Kulshrestha The document discusses various types of data visualizations, particularly focusing on bar charts, line charts, scatter plots, box plots, pie charts, tree maps, and heat maps, emphasizing their appropriate applications and best practices. It highlights the importance of clear, accurate visual representation of data, avoiding common pitfalls such as starting axes at non-zero values and misusing pie charts. Overall, the aim of data visualization is to enhance understanding and insight from complex datasets.
Inferential Statistics
Inferential StatisticsKush Kulshrestha The document discusses inferential statistics, which involves drawing conclusions about a population from sample data, emphasizing the distinctions between populations and samples. It details various sampling methods, such as probability and non-probability sampling, and explains concepts like the sampling distribution and standard error. Additionally, it introduces the central limit theorem and discusses hypothesis testing and confidence intervals, as well as the significance of degrees of freedom in statistical estimates.
Descriptive Statistics
Descriptive StatisticsKush Kulshrestha This document provides an overview of descriptive statistics. It discusses key topics including measures of central tendency (mean, median, mode), measures of variability (range, IQR, variance, standard deviation, skewness, kurtosis), probability and probability distributions (binomial distribution), and how descriptive statistics is used to understand and describe data. Descriptive statistics involves numerically summarizing and presenting data through methods such as graphs, tables, and calculations without inferring conclusions about a population.
Scaling and Normalization
Scaling and NormalizationKush Kulshrestha Scaling transforms data values to fall within a specific range, such as 0 to 1, without changing the data distribution. Normalization changes the data distribution to be normal. Common normalization techniques include standardization, which transforms data to have mean 0 and standard deviation 1, and Box-Cox transformation, which finds the best lambda value to make data more normal. Normalization is useful for algorithms that assume normal data distributions and can improve model performance and interpretation.
Wireless Charging of Electric Vehicles
Wireless Charging of Electric VehiclesKush Kulshrestha The document discusses the concept of electric vehicles (EVs) and the growing need for them due to environmental concerns and the depletion of fossil fuels. It highlights challenges such as battery technology, range anxiety, and inadequate charging infrastructure, while exploring potential solutions like wireless charging through magnetic resonance coupling. Successful case studies and future advancements for efficient power transmission systems for EVs are also presented.
Time management
Time managementKush Kulshrestha Time management is the act of consciously controlling how time is spent on activities to increase productivity, effectiveness, and efficiency. It involves using skills and tools to help accomplish tasks and work towards goals and deadlines. The document provides tips for effective time management, including preparing a schedule with priorities, balancing efforts, focusing on most productive times, taking breaks, tracking progress, reassessing tasks, and getting proper sleep.
Handshakes and their types
Handshakes and their typesKush Kulshrestha The document discusses various types of handshakes and their psychological implications, highlighting how they convey trust, balance, and personality traits. It categorizes handshakes into ten distinctive types, including 'the wet fish', 'the bone crusher', and 'the hand hug', each representing different attitudes and social dynamics. It concludes with the appropriate technique for performing a handshake to ensure effective communication.
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Machine Learning Algorithm - Logistic Regression
- 1. Lesson 12 Machine Learning – Logistic Regression Kush Kulshrestha
- 2. Introduction • One of the most simple algorithm used for 2 class classification problem. • Logistic Regression can be used for various classification problems such as spam detection. Diabetes prediction, if a given customer will purchase a particular product or will they churn another competitor, whether the user will click on a given advertisement link or not. • Just like Linear Regression uses optimize Least Square function to calculate the coefficients, Logistic Regression uses Log Likelihood function to find the value of the best fit parameters. • Logistic Regression is the basic algorithm used to define a Neural Network.
- 3. Sigmoid Function Refresher The logistic function or a Sigmoid function is a function which takes any real value between zero and one. It is defined as y = 1 / (1 + e-x) where y is the scaled value and x is the input value. The logistic function transforms the original range of [-,] to [0,1] and also has a linear part on the transform.
- 4. Probability, Odds and Log odds Probability of an event = (No of Success / Total no of cases) Odds = (One outcome / all other outcomes) Examples: Conversion: • For rolling a dice and getting 1 – Probability = (1/6) Odds = (1/5) • Rolling a dice and getting an even number – Probability = (3/6) = 0.5 Odds = (3/3) = 1 • Dice roll less than 5 – Probability = (4/6) = (2/3) Odds = (4/2) = 2
- 5. What is Logistic Regression Linear regression is continuous response is modelled as a linear combination of the features. In Logistic regression, log-odds of a categorical response being "true" (1) is modelled as a linear combination of the features. This is called the logit function. The equation can be rearranged into the logistic function: p = Conclusion – • Logistic regression outputs the probabilities of a specific class. • Those probabilities can be converted into class predictions. • The logistic function takes on an "s" shape and is bounded by 0 and 1.
- 6. Interpreting Logistic Regression Coefficients Final equation of Logistic regression is: Interpreting beta: ‘1’ unit increase in ‘x' is associated with a B1 unit increase in the log-odds of event for which p is probability of. Log odds are: Hence, positive coefficients increase the log-odds of the response (and thus increase the probability), and negative coefficients decrease the log-odds of the response (and thus decrease the probability).