training datasets

Proven Tasks and Methods for Training Datasets

Training data set is essential for machine learning. The success of machine learning is directly related to the volume and quality of the raw data. The more data, the better the learning process will be. Once the training datasets process is complete, the model will be able to perform the tasks without the need for additional tools.

Types of Learning Tasks with Datasets

Training datasets

There are five types of machine learning data set tasks:

  • Regression task – a prediction is constructed based on a sample of objects with different features. The result of such a task is a number. An example may be the value of securities or real estate after a certain period (half a year or a month).
  • A classification task – based on a set of features, a definite answer is given. The task does not allow getting an infinite number of answers. Most often, it is “yes” or “no”. For example, a system may answer the question of whether a person is represented in a photo
  • The clustering task arranges all the datasets for projects into groups according to a certain attribute. For example, all celestial bodies are divided into stars and planets; all clients of Internet providers are divided into groups according to tariffs and location
  • The dimensionality reduction task – a large number of attributes is reduced to a much smaller number. At most, two or three most often. It allows further visualization to be more comfortable and faster
  • The task of detecting anomalies – at first sight, this task is very similar to classification, but there are certain differences. Anomalies are considered rather rare phenomena, so there are few training examples. A more complex mechanism than classifications needs to be employed to identify such elements effectively.
  • Each type of task addresses a specific range of issues. In this way, a more accurate result is achieved. The system can perform its functions faster, more accurately, and more smoothly.

Methods for Analyzing and Applying Training Datasets

The list of methods for analyzing training datasets includes the following:

  • decision tree – based on a tree-like graph. The model evaluates decisions by considering several factors: efficiency, cost of resources, possible consequences, and probability of occurrence of a certain event. The tree is constructed using a minimum number of questions, the answer to which is either yes or no. Once the questions are answered, the problem can be structured and systematized. A decision is made based on data preprocessing analysis and logical conclusions;
  • naive Bayesian classification is a technique that is needed to solve several problems, such as searching for spam, face recognition, the emotional coloring of text, and linking news to thematic headings. This technique refers to a variety of simple classifiers. In the process of working with train validation test is used;
  • logistic regression – the method allows determining dependencies between variables under the condition that one is categorically dependent, unlike the others. This method is necessary in such cases as credit scoring construction or forecast of profit from the sale of particular goods. Also, this method is excellent for measuring the success of an advertising campaign;
  • Assembly method – considered a very powerful tool. Complicated with two additional algorithms, so it can convert weak models to strong ones, assemble complex classifiers, teach the basics, to correct errors in the output coding;
  • Clustering method – objects are categorized according to certain attributes or characteristics. Therefore, each cluster contains only similar objects. This method is relevant in sociological studies, biology and IT;
  • Principal components method – the purpose of this method is to translate observations on variables that are interrelated with each other into a set of principal components. It is used when there is a need to simplify and minimize data to facilitate learning. It will not work if the data are not properly ordered;
  • Singular decomposition – in particular cases, it fulfills the role of the principal component method. The idea is to decompose a rectangular matrix consisting of numbers, real or complex.

The last method was independent component analysis. With this method, a Java programmer can identify the hidden factors influencing the signals and the hidden values. 

Best Practices for Developing Training Datasets

Training datasets can be obtained from Google research, which gathers all the most important data from a wide range of topics. Amazon and Microsoft Research data sets will also be interesting. For narrower, topic-specific datasets, the likes of xView, Google Open Images, and Stanford Dogs Dataset can be used for computer vision. For text tone analysis Multi Domain sentiment analysis dataset, Stanford Sentiment Treebank, Twitter US Airline Sentiment would be excellent.

Open datasets such as Amazon Reviews, Google Books, Ngrams, and Wikipedia Links data can be used if natural language processing training is needed. Medical data are collected in MIMIC-III. 

Today, the work on developing and implementing new regulations for creating datasets continues. Many interested market players are exploring new possibilities and sources that will make training datasets more informative. The result will be easier access to different data, which means that artificial intelligence training will be much more efficient and faster.

Visualization and Interpretation of Training Datasets

Data visualization is data representation in tables, charts, graphs, and maps. Numerical data, being very complex and large-scale, are much easier to process if they take an understandable form. Visual data helps to draw practical conclusions from raw data. Moreover, it transforms the numbers into a story, giving it context and making it easier to understand by different audiences.

Visualization of online data sets is important for a whole list of resources on the web. In particular, this kind of processing of training data is necessary for social media, smart devices, various websites and internal data collection systems. The whole point is that raw data is extremely difficult to use. Therefore, experts do the work of processing, giving the data a visual form, identifying the relationships between them, and trying to detect patterns and trends.


Many businesses that are quite large in size and turnover need help to react quickly to all the changes in the industry. Thanks to the training dataset, it is possible to adjust almost all production and organizational aspects, optimize processes and receive and process reports from various structures on time. The availability of various methodologies, tasks, and data sets that have already been collected makes it possible to start and successfully implement machine learning.

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