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Ensemble Technique
Combination of the decisions from multiple models to improve the overall performance. Ensemble methods usually produce more accurate solutions than a single model would. There are many ensembling methods both for regression and classification models such as bagging, stacking, and boosting. Bagging: Bagging involves fitting many decision trees on different samples of the same dataset and averaging the predictions.
Stacking: Stacking involves fitting many different models types on the same data and using another model to learn how to best combine the predictions.
Boosting: Boosting involves adding ensemble members sequentially that correct the predictions made by prior models and output a weighted average of the predictions.
Forecasting
Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. It has applications in a wide range of fields where estimates of future conditions are useful. It is a sub-discipline of prediction in which we use time-series data to make forecasts about the future.
Relational Data
Usually in the form of a set of tables or database, where data
points can be related to one other using a unique identifier, so
that the data can be accessed or reassembled in many
different ways.
SQL
It is a computer language for storing, manipulating and retrieving data stored in a relational database. SQL is one of the most widely used query languages over databases. It has many uses like: 1. Allows users to access data in the relational database management systems.
2. Allows users to describe the data.
3. Allows users to define the data in a database and manipulate that data.
4. Allows embedding within other languages using SQL modules, libraries & pre-compilers. 5. Allows users to create and drop databases and tables.
6. Allows users to set permissions on tables, procedures, and views.
SVM
A support vector machine is a supervised machine learning algorithm used for both regression and classification problems. The advantages of support vector machines are:
1. Effective in high-dimensional spaces.
2. Still effective in cases where a number of dimensions is greater than the number of samples.
3. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
4. Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
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