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Datasets for Machine Learning, Term Paper Example

Pages: 2

Words: 615

Term Paper

Introduction

In machine learning, datasets are an aggregated collections of data representing each variable in the corresponding database. Datasets are critical components in machine learning that enables the machine learning algorithm to train the datasets using supervised and unsupervised learning techniques. The datasets are critical for developing unique machine learning repository for implementing facial recognition systems, computer vision systems and using the datasets repository to solve problems.

When implementing the machine learning algorithm, I’ll use Exploitation of data related to workers’ stress, distractions, and productivity in the knowledge economy datasets to train the datasets using appropriate algorithms such as predictions, classification and clustering techniques to train the dataset. Additionally, the train datasets enable the machine learning algorithms to create an appropriate classification metrics and labelling data sets to learn and produce the expected outputs. The test data sets are used to evaluate how well an algorithm was trained and create validation sets for the datasets. The merits of implementing the machine learning datasets includes:

  • Defines the problem statement for the datasets.
  • Prepare the datasets appropriately
  • Evaluate the algorithms (clustering, prediction and classification) and obtain the expected results
  • Improve the results constraints for the expected outputs
  • Present the results constraints for the machine learning datasets.

Training the Datasets using training machine learning algorithm

Clustering technique involves grouping the data points and using the clustering algorithm to classify each data points into a specific group. The clustering parameters contains the subsets (clusters) so that observation in the same clusters.

When implementing the algorithm, the datasets are grouped into clusters through unsupervised learning techniques. Clustering algorithm selects the number of clusters and create appropriate number of appropriate observation within the datasets.

The first process of the imports the datasets and factors variables based on discrete data instances. Additionally, the datasets are critical for creating observational metrics

The process of executing clustering algorithm includes:

  • Choose the group within the clusters randomly
  • Minimize the distance between the clusters centers and group observation
  • Minimize the distances between the group observations
  • Repeat the algorithm until no observation group changes.
  • Prediction

Implementing prediction algorithm entails developing concrete understanding of the datasets. The focus of the datasets includes developing metrics to develop exploratory analysis for the datasets to evaluate the relationships of the variables and the impacts of the outcomes.

Building a linear regression to predict the outcome based on the confidence levels and the intercepts

Additionally, the machine learning predictive algorithm builds a models with clearly independent variables. The designed model optimizes the models to give better metrics within the datasets with the aim of increasing the accuracy of the machine learning algorithm.

Classification (Logistic Regression)

This algorithm technique entails developing binary classification that contains datasets about data records repository with quantitative, textual and ancillary datasets with parameters distributed through the CSV extensions.

The test algorithm entails developing test datasets that spot checks on how to effectively train the datasets using random samples.

The datasets are effectively trained and requires real-time preprocessing of the datasets significantly transform & scale the datasets appropriately.

  1. Development of predictive models for the datasets
  2. Comparing the models and designing an appropriate predictive modelling
  3. Classification algorithm

Classification algorithm entails categorizing the datasets into a desired and distinct number of classes instances and assigning each label to each classes. The classification algorithm uses supervised learning approach in which the computer program learns the functionality from the data inputs and uses this learning technique to classify new observations.

  • Computer vision
  • Data preprocessing
  • Speech recognition
  • Document classification
  • Biometric identification
  • Vector quantization

References

Commonly used Machine Learning Algorithms (with Python and R Codes). (2019, December 3). Retrieved February 8, 2020, from https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/

Gatto, L. (2019, April 24). Chapter 2 An Introduction to Machine Learning with R | An Introduction to Machine Learning with R. Retrieved February 8, 2020, from https://lgatto.github.io/IntroMachineLearningWithR/an-introduction-to-machine-learning-with-r.html

How to perform a Logistic Regression in R. (2015, September 13). Retrieved February 8, 2020, from https://www.r-bloggers.com/how-to-perform-a-logistic-regression-in-r/

Implementation of 17 classification algorithms in R. (n.d.). Retrieved February 8, 2020, from https://www.datasciencecentral.com/profiles/blogs/implemetation-of-17-classification-algorithms-in-r

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