Introduction to machine learning
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The document provides an introduction to machine learning, covering its definitions, types (supervised and unsupervised learning), models, and applications across various fields such as healthcare, finance, and education. It also delves into specific algorithms like linear regression, decision trees, and clustering methods, highlighting their use cases, advantages, and challenges, along with performance metrics for evaluating models. Additionally, it discusses techniques for feature engineering and optimization methods that enhance model accuracy and efficiency.
![Applications
• Trendline --- A trend line represents a trend, the long-term movement in time series data after other
components have been accounted for. It tells whether a particular data set (say GDP, oil prices or stock prices) have
increased or decreased over the period of time.
• Epidemiology --- Early evidence relating tobacco smoking to mortality and morbidity came from observational
studies employing regression analysis. In order to reduce spurious correlations when analyzing observational data,
researchers usually include several variables in their regression models in addition to the variable of primary interest.
• Finance --- The capital asset pricing model uses linear regression as well as the concept of beta for analyzing and
quantifying the systematic risk of an investment. This comes directly from the beta coefficient of the linear regression
model that relates the return on the investment to the return on all risky assets.
• Economics --- Linear regression is the predominant empirical tool in economics. For example, it is used to
predict consumption spending,[20] fixed investment spending, inventory investment, purchases of a
country's exports,[21] spending on imports,[21] the demand to hold liquid assets,[22] labor demand,[23] and labor
supply.[23]
https://en.wikipedia.org/wiki/Linear_regression](https://image.slidesharecdn.com/introtoml-200626174707/85/Introduction-to-machine-learning-12-320.jpg)
![Classification
http://cs229.stanford.edu/notes2020spring/cs229-notes1.pdf
Values of Y, i.e the response can take discrete values
o Binary Classification – response can belong to two
classes (0,1)
üRating – thumbs up, thumbs down.
o Multi-class classification – there can be n classes à
[1, …. n]
ü Movie/restaurant rating can range from
[1,2,3,4,5]
o Multi-class , multi – label classification.
üExample –
ØConcept space in Probability has many
concepts (classes) – [Probability, Bayes
Theorem, DiscretePDF – Binomial, Poisson,
Continuous PDF – normal, exponential, …]
ØA question will typically belong to multiple
classes.](https://image.slidesharecdn.com/introtoml-200626174707/85/Introduction-to-machine-learning-13-320.jpg)
Introduction to machine learning
- 1. Introduction to Machine Learning Sanghamitra Deb Staff Data Scientist Chegg Inc
- 2. Outline • Introduction • Types of Learning • Machine Learning Models • Definition • Optimization • Regularization • Metrics • Feature Engineering • Real World Examples
- 3. What is Machine Learning? • “Learning is any process by which a system improves performance from experience.” - Herbert Simon • Machine learning is training computers to effectively achieve a performance criterion using examples or historical data.
- 4. Why? Machine Learning is used when § Human expertise is unavailable (space expeditions). § Human expertise is not explicable (speech translation). § Information need to be personalized (education, medicine). § Domains with huge amount of data.
- 5. Applications • Education --- Developing Learning Paths for Students • Healthcare --- Personalized Medicine • Retail --- Product recommendations • Web --- Search • Manufacturing --- robotics, control • Finance – fraud detection, asset management • HR --- people analytics • Medical --- drug discovery, automated diagnosis • ………..
- 6. Types of Learning Supervised Unsupervised § Examples or training data is available o Human annotations, user Interactions § Data contains features correlated with the desired outcome § A model is learned from the examples § Goal of the model is to predict future behavior § Direct examples are not available § Data contains features correlated, outcome may not be defined. § It is possible to create clusters correlated with the learning objective based on patterns in the data Learning Objective ---
- 7. Types of Learning Supervised Unsupervised § Regression § Linear § Decision Trees § Classification § Logistic Regression § Naïve Bayes § SVM § Decision Trees – RF, GBDT § Clustering --- kmeans § Similarity based results § Transfer Learning Models ---
- 8. Linear Regression Linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but has been borrowed by machine learning. It is both a statistical algorithm and a machine learning algorithm. Linear Regression | simple linear regression | Ordinary Least Squares | multiple linear regression • The dependent variable Y has a linear relationship to the independent variable X • For each value of X, the probability distribution of Y has the same standard deviation σ. • For any given value of X, The Y values are independent, as indicated by a random pattern on the residual plot. • The Y values are roughly normally distributed (i.e., symmetric and unimodal). Example: Sales Prediction à company’s advertising spend on radio, TV, and newspapers.
- 9. Cost Function Mean Squared Errors To minimize MSE we use Gradient Descent to calculate the gradient of our cost function. Gradient Descent is an algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In linear regression gradient descent is used to update the parameters or weights of the linear model https://ml-cheatsheet.readthedocs.io/en/latest/gradient_descent.html Learning Rate : The size of these steps is called the learning rate. With a high learning rate we can cover more ground each step, but we risk overshooting the lowest point since the slope of the hill is constantly changing.
- 10. Cost Function https://ml-cheatsheet.readthedocs.io/en/latest/gradient_descent.html
- 11. Linear Regression using scikit-learn https://stackabuse.com/linear-regression-in-python-with-scikit-learn/ Data set : https://drive.google.com/file/d/1oakZCv7g3mlmCSdv9J8kdSaqO5_6dIOw/view 4.64, which is less than 10% of the mean
- 12. Applications • Trendline --- A trend line represents a trend, the long-term movement in time series data after other components have been accounted for. It tells whether a particular data set (say GDP, oil prices or stock prices) have increased or decreased over the period of time. • Epidemiology --- Early evidence relating tobacco smoking to mortality and morbidity came from observational studies employing regression analysis. In order to reduce spurious correlations when analyzing observational data, researchers usually include several variables in their regression models in addition to the variable of primary interest. • Finance --- The capital asset pricing model uses linear regression as well as the concept of beta for analyzing and quantifying the systematic risk of an investment. This comes directly from the beta coefficient of the linear regression model that relates the return on the investment to the return on all risky assets. • Economics --- Linear regression is the predominant empirical tool in economics. For example, it is used to predict consumption spending,[20] fixed investment spending, inventory investment, purchases of a country's exports,[21] spending on imports,[21] the demand to hold liquid assets,[22] labor demand,[23] and labor supply.[23] https://en.wikipedia.org/wiki/Linear_regression
- 13. Classification http://cs229.stanford.edu/notes2020spring/cs229-notes1.pdf Values of Y, i.e the response can take discrete values o Binary Classification – response can belong to two classes (0,1) üRating – thumbs up, thumbs down. o Multi-class classification – there can be n classes à [1, …. n] ü Movie/restaurant rating can range from [1,2,3,4,5] o Multi-class , multi – label classification. üExample – ØConcept space in Probability has many concepts (classes) – [Probability, Bayes Theorem, DiscretePDF – Binomial, Poisson, Continuous PDF – normal, exponential, …] ØA question will typically belong to multiple classes.
- 14. Logistic Regression -- Binary Classification Logistic Function or Sigmoid function https://sebastianraschka.com/faq/docs/logistic-why-sigmoid.html -- discussion on the logit function Interpretation: Output is the probability that y=1 given x. Output > 0.5 represents y =1 and output < 0.5 represents y=0
- 16. Multinomial Logistic / Softmax Sigmoid gets replaced by the softmax function. This applies when there are 1,…k classes.
- 17. Regularization --- Linear /Logistic • Penalty against complexity o model does not up "peculiarities," "noise," or "imagines a pattern where there is none.” • Helps with generalizing to new data sets. • Addition of a bias to the model when suffers from high variance or overfitting L2 regularization L1 regularization
- 18. Data --- Train, Test and Validation Train – 90% , Test –5 %, Validation –5% ---- the percentages can vary depending on the total size of the dataset. • Train --- data used to train the model • Validation --- data that the model has not seen but is used for parameter tuning, i.e the model is optimized based on performance on this set. • Test --- model has not seen this data, this data is not used in any part of the computation. Final performance metrics are reported on this data.
- 19. Bias – Variance Tradeoff BIAS — When we model in a very simple, novice way, for example just a single linear equation prediction for an actual complex model. Of course because of this Model becomes Under fit and miss out various important insights and relations between variables. VARIANCE — On the other hand, when we become over concern for a simple given data and fit a model in a very complicated way, it results in Over fit. So each noise and outlier will be considered as valid data point and modelled accordingly.
- 20. Decision Tree Advantage --- • Results are interpretable • Works for both numerical and categorical data • Does not require feature transformations (example --- normalization, scaling) • Robust to Multicollinearity – correlated features. Single decision are rarely used in practice • Unstable --- small changes in data can lead to large structural changes in the decision tree • Prone to overfitting • Easily becomes complex
- 21. Ensemble Tecniques --- Random Forest Many trees are better than one! N slightly differently trained decision trees and merges them together to get more accurate and stable predictions.
- 22. Regularization • Limit tree depth. • Pruning • Penalize selection of new features over features that have similar gain • Set stricter stopping criterion on when to split a node further (e.g. min gain, number of samples etc.) Feature Importance A feature’s importance score measures the contribution from the feature. It is based on the impurity reduction of the class due to the feature. Bias towards features with more categories It is important to compute the correlation with accuracy.
- 23. Unsupervised Learning There is no training data …
- 24. Clustering • Hierarchical clustering • K-means clustering • K-NN (k nearest neighbors) Clustering is a technique that finds groups (clusters) in the data that have similar patterns.
- 26. Similarity based Recommendations • Text data – news article, study material , books, … • For every piece of content compute the distance to all every other content in the cluster --- save the top –n in a database • When a user views any content surface the top-n (typically 5) other similar items
- 27. Applications • Recommendations based on Text Similarity • Customer Segmentation • Content Categorization • As a pre-analysis for supervised learning
- 28. Performance Metrics Is the model good enough?
- 29. Regression R² Error: The metric helps us to compare our current model with a constant baseline and tells us how much our model is better. The constant baseline is chosen by taking the mean of the data and drawing a line at the mean. Adjusted R²: Adjusted R² depicts the same meaning as R² but is an improvement of it. R² suffers from the problem that the scores improve on increasing terms even though the model is not improving which may misguide the researcher. Adjusted R² is always lower than R² as it adjusts for the increasing predictors and only shows improvement if there is a real improvement
- 30. Classification True Positive --- Number of observations that model correctly predicts the positive class False Positive --- Number of observations where model incorrectly predicts the positive class. False Negatives --- Number of observations where model incorrectly predicts the negative class. True Negatives --- Number of observations where model correctly predicts the negative class https://en.wikipedia.org/wiki/Precision_and_recall
- 31. Classification https://en.wikipedia.org/wiki/Precision_and_recall Precision : TP/(TP+FP) --- what percentage of the positive class is actually positive? Recall : TP/(TP+FN) --- what percentage of the positive class gets captured by the model? Accuracy --- (TP+TN)/(TP+FP+TN+FN) --- what percentage of predictions are correct?
- 32. Thresholding --- Coverage In a binary classification if you choose randomly the probability of belonging to a class is 0.5 0.3 0.7 It is possible improve the percentage of correct results at the cost of coverage.
- 33. Confusion Matrix
- 34. ROC & AUC ROC – Reciever Operating Characteristics An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. AUC – Area Under the Curve. • AUC is scale-invariant. It measures how well predictions are ranked, rather than their absolute values. • AUC is classification-threshold-invariant. It measures the quality of the model's predictions irrespective of what classification threshold is chosen. https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc • TPR = TP/(TP+FN) • FPR = FP/(FP+TN) Random
- 35. Feature Engineering Combining math and intuition
- 36. Imputation or missing values https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114#1c08 Drop rows with missing values Cons: Reduces training data. In case of multiple features values may only be missing in a subset of features. Replace with a reasonable value – median is common Cons: There are assumptions used when values are missing which may not be correct. Categorical Imputation --- replace with the most common value. Cons: Assumptions
- 38. Binning The trade-off between performance and overfitting is the key point of the binning process.
- 39. Log Transform • It helps to handle skewed data and after transformation, the distribution becomes more approximate to normal. • In most of the cases the magnitude order of the data changes within the range of the data. For instance, the difference between ages 15 and 20 is not equal to the ages 65 and 70. In terms of years, yes, they are identical, but for all other aspects, 5 years of difference in young ages mean a higher magnitude difference. This type of data comes from a multiplicative process and log transform normalizes the magnitude differences like that. • It also decreases the effect of the outliers, due to the normalization of magnitude differences and the model become more robust.
- 40. One-hot encoding
- 41. Scaling -- all features have the same range The continuous features become identical in terms of the range, after a scaling process. This process is not mandatory for many algorithms, but it might be still nice to apply. However, the algorithms based on distance calculations such as k-NN or k-Means need to have scaled continuous features as model input.
- 42. Normalization Normalization (or min-max normalization) scale all values in a fixed range between 0 and 1.
- 43. Standardization
- 44. Real World Application There is no training data …
- 45. Use Case --- Predict will a user buy a book? Online Book Store
- 46. Generating Features Book Features User Features Book-User Features • Tags --- genre, subject, … • Level – Beginner, Intermediate Advanced • Popularity score – 1. exponential decay on clicks 2. Create time bound scores, such number of views in the last 7 days, last 14 days • Price • Length of the book • … • Tags --- genre, subject, … • Level – Beginner, Intermediate Advanced Derived from user interactions on the site. • View Score 1. Exponential decay clicks on books 2. Time bound --- number of views in past 7/14 days • Price category • Time of day – categorical • … • Number of Views in past 14/30 days • Already bought • Number of views from the same author • …
- 47. Response Variable & Modeling • If the site is not super active you might not have enough data on purchases • Multi-stage model • Stage 1 : response variable views – i.e will the user view/click on this book in the next 3 days? • Stage 2: response variable purchase – will the user purchase this book in the next 3 days. The probability of view will be a feature in the Stage 2 model. Modeling: We have a mix of categorical and numerical features --- Random Forest
- 49. Appendix
- 50. K-Nearest Neighbors K-Nearest Neighbor is a classification algorithm that leverages observations close to the target point to decide which class it belongs to There are two parts of the algorithm: first, how to measure “close”; second, how many close observations (K) we need. https://github.com/spotify/annoy
- 51. KNN - algo
- 52. Derivative of the sigmoid & cost function
- 53. Metrics related to Ranking • MRR • MAP • NDCG
- 55. Loss Functions • Cross-Entropy • Hinge • Huber • Kullback-Leibler • MAE (L1) • MSE (L2)
- 56. Optimization Techniques • Gradient Descent oStochastic oMini Batch • Adagrad • RMSprop • Adam • Cross-Entropy • Others
- 57. Decision Tree Based Regression Pros: Decision trees can handle both categorical and numerical data. Cons: Does not handle feature interaction very well.