Goldhat Brain Principles
- Distributed cluster ensure support for as much open source knowledge sharing as possible, especially support sharing knowledge regarding environmental, health and development
- Leverage existing infrastructure projects
- Use Open-source and exists software, cloud services
- Encrypt and scramble data
- AS STANDARD – USE ALAS – NO personal identifying data, should be shared especially for profit or sale to third parties,
The following is adapted from the following great website “https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html/2”
Goldhat Decision Trees Analysis (G-DTA): Fuzzy and non-fuzzy decision tree analysis is a decision support technique that uses a ‘tree-like’ graph or model of possible decisions and their possible consequences, including chance-event outcomes, resource and financial costs, as well as benefits and utility.
Naive Bayes Classification: Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. The featured image is the equation — with P(A|B) is posterior probability, P(B|A) is likelihood, P(A) is class prior probability, and P(B) is predictor prior probability.
- To mark an email as spam or not spam
- Classify a news article about technology, politics, or sports
- Check a piece of text expressing positive emotions, or negative emotions?
- Used for face recognition software.
3. Ordinary Least Squares Regression: If you know statistics, you probably have heard of linear regression before. Least squares is a method for performing linear regression. You can think of linear regression as the task of fitting a straight line through a set of points. There are multiple possible strategies to do this, and “ordinary least squares” strategy go like this — You can draw a line, and then for each of the data points, measure the vertical distance between the point and the line, and add these up; the fitted line would be the one where this sum of distances is as small as possible.
4. Logistic Regression: Logistic regression is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables. It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution.
- Credit Scoring
- Measuring the success rates of marketing campaigns
- Predicting the revenues of a certain product
- Is there going to be an earthquake on a particular day?
5. Support Vector Machines: SVM is binary classification algorithm. Given a set of points of 2 types in N dimensional place, SVM generates a (N — 1) dimensional hyperplane to separate those points into 2 groups. Say you have some points of 2 types in a paper which are linearly separable. SVM will find a straight line which separates those points into 2 types and situated as far as possible from all those points.
6. Ensemble Methods: Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a weighted vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, bagging, and boosting.
- They average out biases: If you average a bunch of democratic-leaning polls and republican-leaning polls together, you will get an average something that isn’t leaning either way.
- They reduce the variance: The aggregate opinion of a bunch of models is less noisy than the single opinion of one of the models. In finance, this is called diversification — a mixed portfolio of many stocks will be much less variable than just one of the stocks alone. This is why your models will be better with more data points rather than fewer.
- They are unlikely to over-fit: If you have individual models that didn’t over-fit, and you are combining the predictions from each model in a simple way (average, weighted average, logistic regression), then there’s no room for over-fitting.
7. Clustering Algorithms: Clustering is the task of grouping a set of objects such that objects in the same group (cluster) are more similar to each other than to those in other groups.
- Centroid-based algorithms
- Connectivity-based algorithms
- Density-based algorithms
- Dimensionality Reduction
- Neural networks / Deep Learning
8. Principal Component Analysis: PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
9. Singular Value Decomposition: In linear algebra, SVD is a factorization of a real complex matrix. For a given m * n matrix M, there exists a decomposition such that M = UΣV, where U and V are unitary matrices and Σ is a diagonal matrix.
10. Independent Component Analysis: ICA is a statistical technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. In the model, the data variables are assumed to be linear mixtures of some unknown latent variables, and the mixing system is also unknown. The latent variables are assumed non-gaussian and mutually independent, and they are called independent components of the observed data.
Now go forth and wield your understanding of algorithms to create machine learning applications that make better experiences for people everywhere.
Bio: James Le is a Product Intern at New Story Charity and a Computer Science and Communication student at Denison University.
Original. Reposted with permission.
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This is taken from “https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html/2”
- Use probabilistic automation and next closest match and machine learning to pre-config automation as standard to improve scenario planning but require opt-in to access
- Iterative virtual environments, improvement and reinforcement pathways
- Alternative scenarios simulation
- Alternative configuration simulation –
- Binary decision tree – simulation
- Occam’s razor
- (Monti ?) Carlo Simulation/ Finite difference methods or Binomial/trinomial trees
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