Differences between Machine Learning vs Statistics
Machine learning is a subset of artificial intelligence sectors where you let the machine train upon itself and get the prediction results. Machine learning is simply training data using algorithms. Sometimes it is also a black box for most of the data analysts. You are training the machine (Computer or model) with the set of rules you have (data points). Statistics is a branch of mathematics where you derive patterns in the data using mathematical solutions. Statistics is pure mathematics. To derive any insights or correlations between the data, there are some geometrical patterns that could be identified and it is derived using mathematical practices (statistics).To identify the pattern, statistics come into the picture.
Let us study much more about Machine Learning and Statistics in detail:
In simple words or notations, you give the machine some conditional based If X1 = <some value> and X2 = <some values> then Y=estimator. Similarly, many data points are combined in order to get the estimator or the predictor. This is what the machine does by itself. It trains with all the data fed and when new values are given, it automatically gives the estimator.
Before feeding the data to the machine, it is very important to understand the data and identify any correlations and patterns. If there is a correlation between two data points or more then it as high relevancy in giving the right prediction.
In the world of artificial intelligence now, most of the companies are heading towards automation, robotics. The base or the fundamentals to lead such domains are statistics, linear algebra, probability, and geometry. This is because data insight or any problem related to data could be solved using mathematics.
Regards to the skillset of machine learning and statistics, and descriptive statistics or statistical modeling are built by the statistician. Whereas the machine learning is about the hypothesis, a classification which requires knowledge of basic programming and data structures and algorithms.
Head to Head Comparison Between Machine Learning and Statistics
Below is the top 10 comparison between Machine Learning and Statistics:
Key Differences Between Machine Learning and Statistics
Below are the lists of points, describe the key Differences Between Machine Learning and Statistics:
1. Machine learning is a branch from the artificial intelligence which deals with the non-human power in achieving the outcomes. Statistics is a subfield of mathematics where it is about derivatives and probabilities inferred from the data.
2. Machine learning is one of the fields in data science and statistics is the base for any machine learning models. To build the model, one has to do the EDA (exploratory data analysis) where statistics play a major role.
3. To build a model the initial stage is to do feature engineering that involves which attributes to be used and which attributes give results on providing the maximum likelihood. In order to derive the right features, a correlation between the independent variables or data points is important to identify.
4. Machine learning vs statistics is not two different wide concepts. They both are associated with one another. Without statistics, one cannot build a model and there is no reason just doing statistical analysis on the data. It leads to building the model.
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5. Even after building the model, to measure the performance and evaluate the results, statistics come in and play a vital role. To measure the performance, there are many evaluation metrics being built-in data science. One such is building confusion matrix algebra where true positives, false negatives, true negatives and false positives are derived.
6. In terms of the applications, machine learning and statistics are coupled in a way that one leads to others.
7. Statistical analysis and machine learning have collaborated in order to apply the data science to the data problem or to get insights from the data which leads to a higher impact on sales or business and marketing.
8. Machine learning is a branch of data science or analytics which leads to automation and artificial intelligence. Statistics is a branch of mathematics where you apply these solutions to the data which leads to predictive modeling etc.
Comparison Table between Machine Learning and Statistics
Following are the lists of points that show the Comparisons Between Machine Learning vs Statistics.
Basis of
Comparison |
Machine Learning | Statistics |
Definition | Machine learning is a set of steps or rules fed by the user where machine understands and train by itself | Statistics is a mathematical concept in finding the patterns from the data. |
Usage | To predict future events or classify an existing material | The relationship between the data points |
Types | Supervised learning and unsupervised learning | Forecasting continuous variables, Regression, classification |
Input-output | Features and labels | Datapoints |
Use cases | For hypothesis | Correlation between the data points, univariate, multivariable |
Ease of use | Mathematics and Algorithms | Mathematics knowledge |
Applications | Weather forecast, topic modeling,
Predictive modeling |
Descriptive statistics, finding patterns, outliers in the data |
Field | Data analytics, Artificial intelligence | Artificial intelligence, data science research labs. |
Stands out | Predominant algorithms and concepts like neural networks | Derivatives, probabilities |
Keywords | Linear regression, Random forest, support vector machine, neural networks | Covariance, univariate, multivariate, estimators, p-values, rmse |
Conclusion
In this modern technology world, artificial intelligence is all about in the market these days. As technology is widening and innovations and ideas pouring, there is a humongous volume of data that are getting generated. When there is data, there needs analytics. Analytics is mainly on how much insights from the data could be derived. As in the traditional RDBMS structured data analytics and the descriptive statistics, there are many insight and outliers being missed or hidden which may be useful in improving the business. Those outliers bring lots of importance in decision making or improving the sales of the products.
Data science is applied to the volume of data that are generated in these years or even on the historical data. The outliers are well used and not ignored where more useful information is gathered to bring out positive results that impact the marketing or improvement in the business. To accomplish any machine learning models or statistical analysis, one definitely needs to know statistics, algorithms, and fundamentals of mathematical concepts. As we are driving to a fast-paced technology, Artificial intelligence is the present and the future.
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