Microeconomics helps us understand how individuals make decisions about what to buy, how much to save, and how much to invest. Microeconomics focuses on individual consumers and businesses, while macroeconomics looks at the economy as a whole.īoth micro and macro economics are important for understanding how the economy works. It is a social science that focuses on the production, distribution, and consumption of goods and services.Įconomics can be divided into two main branches: microeconomics and macroeconomics. The importance of understanding the difference between micro and macro economicsĮconomics is the study of how people use limited resources to satisfy their unlimited wants and needs. It also looks at government policies that can impact the economy, such as taxation and interest rates. Macroeconomics examines economic indicators such as employment, inflation, and Gross Domestic Product (GDP). Macroeconomic analysis, on the other hand, is more broad-based and can be easier to understand.For example, microeconomic analysis is often very technical and can be difficult to apply to real-world situations.Macroeconomics, on the other hand, looks at the economy as a whole and analyses issues such as inflation, unemployment, and economic growth.īoth microeconomics and macroeconomics are essential for understanding how economies work, but they each have their own strengths and weaknesses. Microeconomics focuses on individual economic units, such as households and businesses, and how they make decisions. Microeconomics and macroeconomics are both important fields of study within economics. What is the example of microeconomics and macroeconomics? For this reason, it is important to understand both micro and macro economics.As a result, each perspective can provide insights that the other cannot.Microeconomics focuses on the decisions of individual actors, while macroeconomics looks at the economy as a whole. However, they take different approaches to studying economic activity. It looks at factors such as inflation, unemployment, and economic growth.īoth micro and macro economics are important for understanding how the economy works.
In contrast, macroeconomics is the study of the economy as a whole. Micro-Average & Macro-Average Recall Scores for Multi-class Classificationįor multi-class classification problems, micro-average recall scores can be defined as the sum of true positives for all the classes divided by the actual positives (and not the predicted positives).Finance vs Economics: What’s the Relation Between Them?
The positive prediction is the sum of all true positives and false positives. Micro-Average & Macro-Average Precision Scores for Multi-class Classificationįor multi-class classification problems, micro-average precision scores can be defined as the sum of true positives for all the classes divided by all positive predictions. On the other hand, micro-average can be a useful measure when your dataset varies in size. You should not come up with any specific decision with this average. The macro-average method can be used when you want to know how the system performs overall across the sets of data. The weighted macro-average is calculated by weighting the score of each class label by the number of true instances when calculating the average. Use a weighted macro-averaging score in case of class imbalances (different instances related to different class labels). Use macro-averaging score when all classes need to be treated equally to evaluate the classifier's overall performance concerning the most frequent class labels. Use micro-averaging score when there is a need to weigh each instance or prediction equally.
When to use micro-averaging and macro-averaging scores? The macro-average F1-score is calculated as the arithmetic mean of individual classes’ F1-score. The macro-average precision and recall score is calculated as the arithmetic mean of individual classes’ precision and recall scores. The micro-average precision and recall score is calculated from the individual classes’ true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs) of the model.
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