DASMI Algorithms

Question 1

An unsupervised artificial neural network.
learns how to efficiently compress and encode data then
learns how to reconstruct the data back from the reduced encoded representation to a representation that is as close to the original input as possible.

Answer 1

Autoencoder

Question 2

A series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the HUMAN BRAIN operates

Answer 2

neural network

Question 3

An algorithm that decides how to take a set of other (typically, though not necessarily non-meta) “algorithms” (which might be as dumb as a constant output, for example), and CONSTRUCTS A NEW ALGORITHM OUT OF THOSE TWO, often by combining or weighting the outputs of the component algorithms.

Answer 3

Meta-Algorithm

Question 4

Tiered Series of Meta Algorithms

Answer 4

DASMI Intelligence Model

Question 5

Top algorithm - classification and regression

Answer 5

Support Vector Machine (SVM) Algorithm

Question 6

It works by classifying the data into different classes by finding a line (hyperplane) which separates the training data set into classes.

Answer 6

Support Vector Machine (SVM) Algorithm

Question 7

A statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).

Answer 7

regression

Question 8

Algorithm that renders more efficiency for correct classification of the future data.
Does not make any strong assumptions on data.
Does not over-fit the data.

Answer 8

Support Vector Machine (SVM) Algorithm

Question 9

Algorithm used for association analysis on healthcare data like-the drugs taken by patients, characteristics of each patient, adverse ill-effects patients experience, initial diagnosis, etc.

This analysis produces association rules that help identify the combination of patient characteristics and medications that lead to adverse side effects of the drugs.

Answer 9

Apriori algorithm

Question 10

An algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Answer 10

Apriori algorithm

Question 11

Algorithm principle can reduce the number of itemsets we need to examine. Put simply, the apriori principle states that. if an itemset is infrequent, then all its supersets must also be infrequent. This means that if {beer} was found to be infrequent, we can expect {beer, pizza} to be equally or even more infrequent.

Answer 11

Apriori algorithm

Question 12

2 step process of this algorithm
Join Step: This step generates (K+1) itemset from K-itemsets by joining each item with itself.
•Prune Step: This step scans the count of each item in the database. If the candidate item does not meet minimum support, then it is regarded as infrequent and thus it is removed. This step is performed to reduce the size of the candidate itemsets.

Answer 12

Apriori algorithm

Question 13

Algorithm - Develop prediction performance and risk factors:

Answer 13

Random Forest Algorithm

Question 14

A go-to machine learning algorithm that uses a BAGGING approach to create a bunch of DECISION TREES with random subset of the data.

A model is trained several times on RANDOM SAMPLE of the dataset to achieve good prediction performance

Answer 14

Random Forest Algorithm

Question 15

Ensemble learning method, the output of all the decision trees is combined to make the final prediction.

Answer 15

Random Forest Algorithm

Question 16

To COMBINE the predictions of several models built with a given learning algorithm in order to improve robustness over a single model.

Answer 16

general principle of an ensemble method

Question 17

a method in ensemble for improving unstable estimation or classification schemes. a bunch of decision trees with random subset of the data.

Answer 17

bagging

Question 18

models too closely to a specific set of data – issue may not be weighed toward the violent crime committed but more so the schizophrenia

Answer 18

Overfit

Question 19

If you have individual models that didn’t overfit, and you’re combining the predictions from each model in a simple way (average, weighted average, or logistic regression), then there’s no room for

Answer 19

Overfitting

Question 20

The final prediction of the algorithm is derived by polling the results of each decision tree or just by going with a prediction that appears the most times in the decision trees.

Answer 20

Random Forest

Question 21

a bunch of democratic-leaning polls and a bunch of republican-leaning polls together, you will get on AVERAGE something that isn’t leaning either way

Answer 21

average out biases

Question 22

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 also why your models will be better with more data points rather than fewer.

Answer 22

reduce the variance

Question 23

collects offender history data, applies mitigating factors, references consequential events based upon
timeline, and generates customizable system solutions employable across multiple sectors of the Criminal Justice System.

Answer 23

Hyperdata Algorithmic Scoring System

Question 24

The science behind our smart technology is a set of complex processes involving input classification, association analyzation, and performance
prediction of multiple data structures utilizing dynamic mitigating factors and appropriate event chronology.

Answer 24

Hyperdata Algorithmic Scoring System

Question 25

The core foundation of our technology infrastructure integrates a neural network composed of a series of algorithms that deliver sector specific scoring outcomes.

Answer 25

Hyperdata Algorithmic Scoring System

Question 26

Order of our Tiered Series of Meta Algorithms

Answer 26

Support Vector Machine, Apriori, Random Forest

Question 27

neural network

Answer 27

series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

Question 28

Autoencoder

Answer 28

an unsupervised artificial neural network that learns how to efficiently compress and encode data then learns how to reconstruct the data back from the reduced encoded representation to a representation that is as close to the original input as possible.

Question 29

Overfit

Answer 29

models too closely to a specific set of data

Question 30

bagging

Answer 30

a method in ensemble for improving unstable estimation or classification schemes. a bunch of decision trees with random subset of the data.

Question 31

Support Vector Machine (SVM)

Answer 31

classification/regression algorithms

Question 32

Apriori

Answer 32

Algorithm used for association analysis

Question 33

Random Forest

Answer 33

go-to machine learning algorithm that uses a bagging approach to create a bunch of decision trees with random subset of the data.