2023 is marked by perpetual advancements in AI technology, and by examining the evolution of computing thus far, we can anticipate what lies ahead in the future.
According to CISCO experts, automation continues to replace nearly all manual labor, so the traditional definition of “manual” is undergoing a transformation. With the advent of various Machine Learning algorithms, computers are now capable of playing chess, conducting surgeries, and becoming increasingly intelligent and personalized.
However, what about machine learning techniques for business applications? In this article, we will check machine-learning techniques for business from InData Labs experts, which can be used by your software team to significantly enhance your KPIs. Let’s delve into it!
1. Linear Regression
Linear Regression in machine learning can be better understood by imagining the arrangement of random logs of wood in increasing order of weight. However, there is a twist – you cannot weigh each log and must guess its weight based on its height and girth. Using these visible parameters, you can arrange the logs accordingly. This is the concept behind linear regression.
The process involves fitting independent and dependent variables to a line and establishing a relationship between them. This line is called the regression line and can be represented by the linear equation Y = a * X + b.
Here, Y represents the dependent variable, a represents the slope, X represents the independent variable, and b represents the intercept.
The coefficients a and b are obtained by minimizing the sum of the squared difference between data points and the regression line.
2. Logistic Regression
Logistic Regression is utilized for approximating discrete values, typically binary values such as 0 and 1, from a group of independent variables. Its purpose is to anticipate the likelihood of an occurrence by matching data to a logit function. This method is commonly referred to as logit regression.
The following techniques are frequently employed to enhance logistic regression models:
- integration of interaction terms;
- elimination of features;
- implementation of regularization techniques;
- utilization of a non-linear model.
3. Decision Tree
The Decision Tree algorithm is a widely used supervised learning algorithm in machine learning. It is effective in classifying both categorical and continuous dependent variables by dividing the population into homogeneous sets based on the most significant attributes or independent variables.
4. SVM (Support Vector Machine) Algorithm
The SVM (Support Vector Machine) Algorithm is a classification method that plots raw data as points in an n-dimensional space, where each feature is tied to a specific coordinate. Classifiers called lines can be used to split the data and plot them on a graph. It’s a great example that shows how are machine learning techniques can be effectively used in business apps.
5. Naive Bayes Algorithm
The Naive Bayes Algorithm assumes that the presence of one feature in a class is not related to the presence of any other feature, even if they are related. This classifier considers all features independently when calculating the probability of a particular outcome. A Naive Bayesian model is easy to build and useful for large datasets, and it is known to outperform even highly sophisticated classification methods.
6. KNN (K- Nearest Neighbors) Algorithm
The K-Nearest Neighbors (KNN) algorithm is versatile and can be used for classification and regression problems. However, it is more commonly used for classification in the Data Science industry. KNN is a straightforward algorithm that stores all available cases and classifies new cases based on the majority vote of its k neighbors. The algorithm assigns the new case to the class that it has the most similarities with, which is determined by a distance function. To better understand KNN, it can be compared to seeking information about a person by talking to their friends and colleagues. There are a few things to consider before selecting KNN, such as its computational expense, the need for variable normalization to avoid bias, and the requirement for data preprocessing.
K-Means is an unsupervised learning algorithm that solves clustering problems. It categorizes data sets into a specific number of clusters (referred to as K) so that all data points within a cluster are homogenous and different from those in other clusters. The K-Means algorithm selects K centroids, or points, for each cluster and assigns each data point to the closest centroid, creating K clusters. The algorithm then generates new centroids based on the existing cluster members and determines the closest distance for each data point using these new centroids. This process is repeated until the centroids no longer change.
8. Random Forest Algorithm
The Random Forest Algorithm involves a group of decision trees that work together to classify a new object based on its attributes. Each tree votes for a particular class and the classification with the most votes across all trees is chosen. To grow each tree, a random sample of N cases is taken from the training set, and m variables are selected at random out of M at each node to determine the best split. The trees are grown to their fullest extent without any pruning.
9. Dimensionality Reduction Algorithms
In the current era, large amounts of data are being stored and analyzed by various organizations. As a data scientist, it is important to identify significant patterns and variables within this data. Dimensionality reduction algorithms such as Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest can assist in this process by reducing the number of variables and highlighting relevant details.
10. Gradient Boosting Algorithm and AdaBoosting Algorithm
Boosting algorithms, such as Gradient Boosting Algorithm and AdaBoosting Algorithm, are utilized to handle large amounts of data to achieve precise predictions. These algorithms are part of ensemble learning, which combines several base estimators to enhance the robustness of predictions.
In essence, they combine multiple subpar or average predictors to create a powerful predictor. Boosting algorithms have proven to be effective in data science competitions, such as Kaggle, AV Hackathon, and CrowdAnalytix, making them the most popular machine learning algorithms today. To obtain accurate results, use these algorithms alongside Python and R Codes.
In today’s fast-changing world, you better start your journey toward using machine learning techniques in your business apps without delay, as they can significantly improve your KPIs if implemented properly. With the field rapidly expanding, the earlier you grasp the breadth of available tools, the sooner you can develop solutions to intricate work challenges. Use this article as a quick guide to start from, and, of course, remember to find a trusted custom ML solutions provider that has enough experience with your business niche.