An algorithm is a process or a set of rules that needs to be followed either in calculations or other problem-solving operations, specifically by a computer. The basic goal of any algorithm is to solve/find a solution to a particular problem, which is usually defined as a sequence of a number of steps.
For each and every kind of question, there’s a unique pattern that must be available in the database so as to provide a suitable and apt response. With tons of combinations of patterns, it will create a hierarchical structure. We utilize algorithms for reducing classifiers and try to generate a manageable structure. Scientists state this as a reductionist approach, however, in order to provide a simplified solution, it reduces the number of steps taken.
Naive Bayes is one of the classic algorithms for classification of text and natural language processing. For example, let’s assume a set of sentences that belong to a particular category of class. With the addition of a new input sentence, each word is counted for occurrence and accounted for commonality, and all classes are assigned a score. The class that scores highest is most likely to be associated with the provided input sentence.
Machine Learning studies algorithms, learns from it and makes predictions based on data. The learning is implied by optimizing some parameters of performance when a task is executed through some type of experience. The machine learning algorithms vary in the way they represent candidate programs (e.g. mathematical functions, general programming languages, and decision trees) and how they search through this space of programs (optimization algorithms, evolutionary and exact search methods).
There are two categories of optimization algorithms: traditional deterministic models and modern metaheuristics algorithm. The former method will generate the same results in different runs under the same conditions whereas the latter method will generate different results for different runs even under the same conditions. Modern companies can implement these optimization techniques for reducing their aggregate transportation costs,
thus providing a better service to their customers. The fundamental step in the optimization process is the initialization stage where either the population or parameter can be initialized and the running environments for later search are set during this process.
The next step is to implement global search and local search simultaneously, i.e. exploration and exploitation until the termination condition is reached and we finally get the output.
Typically, there are three general types of Machine Learning: supervised, unsupervised and semi-supervised learning. Supervised learning depends on a set of procedures, where the aim is to map the input data to the desired output, classifying the input data based on their attributes.
Supervised methods are used in classification, regression, time-series prediction, dimension reduction, etc. Unsupervised learning tries to find patterns in the data, i.e. judge the similarity between data for deciding whether the data can be grouped.
Unsupervised methods are time series modeling, anomaly detection, latent variable models, etc. Semi-supervised learning makes use of a small amount of labeled data and a large amount of unsupervised data.
Search engines, recommendation systems, robotics, image recognition, etc. are some of the most conventional examples of machine learning. As the real data generally contain missing, inconsistent and erroneous values, data validation becomes a crucial step in
Machine Learning. The vast amount of unstructured and structured data that is collected and analyzed in real-time, has the potential to improve our understanding of consumer behavior and predict demand accurately. Even though most of the machine learning techniques fall under the supervised learning family where the inputs are labeled, it does not apply to most of the optimization problems because of inaccessibility to optimum labels . However, we can compare a set of results, and provide feedback to the algorithm which can learn before predicting the next set of solutions . Hence, reinforcement learning is generally used for solving combinatorial optimization problems.
For machine intelligence to be accepted, it should interact with humans, exposing its reasoning to humans, and incorporate human feedback in real-time for making future decisions. Machine recommendations are much more appealing when the users understand how they were generated, and when they can provide feedback to those recommendations.
For understanding the functionality of Linear Regression Algorithm, imagine arranging random logs of wood in the increasing order of their weight, however, you just cannot weigh each log. You guess its weight by looking at the height and breadth of the log (through visual analysis) and make arrangements using different combinations of these visible parameters.
Logistic Regression estimates the discrete values (which are generally binary values like 0 or 1) from a diverse set of independent variables. It helps in predicting the probability of an event. It is also termed as logit regression in some cases.
Here are some methods used for improving the logistic regression models:
The most popular machine learning algorithm (supervised learning) that is used for classifying problems is Decision Trees. It classifies categorical dependent and continuous dependent variables. We split the entire population into multiple sets based on the most important attributes or independent variables.
SVM is a classification method where you plot data as points in n-dimensional space (n is the number of features). The value of these features is then assigned to a particular coordinate, that makes it easy for classifying the data. Lines that are called classifiers are used for splitting the data and plotting them on a graph.
This classifier considers the presence of a particular feature in a specific class that is completely different and unrelated to the presence of that feature in the same class. Even when the features are related, this classifier will consider all the properties independently when it calculates the probability of any particular outcome. This model is easy to build and very useful for huge datasets. It is simple and can outperform other highly sophisticated methods of classification.
This algorithm is applied to both regression and classification problems. However, it is generally used to solve classification problems. It stores all the available cases and classifies the new cases by taking into consideration a majority vote from its k neighbors.
K Nearest Neighbors can be easily understood with a real life example - if you want any information about a particular person, then talk to his/her friends and relatives.
It’s an unsupervised algorithm that solves the clustering problems. Data sets are generally classified into a defined number of clusters (K) in a way that all the data points are homogeneous and heterogeneous from the data present in other clusters.
A collection of decision trees model is called a Random Forest. It classifies a new object based on its properties where each tree is classified, and the tree votes for that particular class. However, the forest decides according to the most votes.
Dimensionality reduction algorithms such as Decision Trees, Missing Value Ratio, Factor Analysis, and Random Forest models help you find vast amounts of data that is stored and analyzed by big corporates, government agencies, and various research organizations. The big challenge is to identify significant patterns and variables in the data.
Boosting algorithms like Gradient boosting and Adaboost are used when huge amounts of data needs to be handled for making predictions that have high accuracy. Boosting combines various weak predictors for building a strong predictor. These algorithms work well in competitions like Kaggle, CrowdAnalytix and AV Hackathon. Boosting is one of the most preferred machine learning algorithms today for their accurate outcomes.
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