Of the 8 identified as dogs, 5 actually are do They may also take virtually monitored exams online, where a proctor watches via webcam or where computer software detects cheating by checking the test-takers' screens. — Precision value of 80% means that on average, 2 of every 10 diabetic labeled student by our program is healthy, and 8 is diabetic. Stage Design - A Discussion between Industry Professionals. This article aims to briefly explain the definition of commonly used metrics in machine learning, including Accuracy, Precision, Recall, and F1.. Accuracy is a good basic metric to measure the performance of a model. It sounds complicated but actually pretty simple as we illustrate it with an example. Precision, Recall, and the F1 Score. Know More >> DATA ANALYSIS. • not interested in accuracy on entire dataset • want accurate predictions for 5%, 10%, or 20% of dataset • don’t care about remaining 95%, 90%, 80%, resp. Accuracy indicates, among all the test datasets, for example, how many of them are captured correctly by the model comparing to their actual value. In measurement of a set, accuracy is closeness of the measurements to a specific value, while precision is the closeness of the measurements to each other. The metrics will be of outmost importance for all the chapters of our machine learning tutorial. This is commonly used for medical devices, such as virus testing kits and pregnancy tests. WKMO is the home to all martial arts devotees who put the moral values of their discipline above the rules of an intense organization. And also if the “A” is recognized as “notA” or vice versa will be a “False” outcome. Get Free Accuracy Vs Precision Machine Learning now and use Accuracy Vs Precision Machine Learning immediately to get % off or $ off or free shipping. Let's assume if y_hat >= 0.5, we decide our prediction is female(1). In pattern recognition, information retrieval and classification, precision is the fraction of relevant instances among the retrieved instances, while recall is the fraction of retrieved relevant instances among all relevant instances. Bottom Line is — Accuracy value of 90% means that 1 of every 10 labels is incorrect, and 9 is correct. It’s no different for deep learning than it’s for any other machine learning method. However, I got different answers (0.89 vs 0.93, respectively). Imagine you have a Snake classification data set. So, four types of classes can be recognized. So, the data availability might be biased over the population. In their book Applied Predictive Modeling, Kuhn and Johnson comment early on the trade-off of model prediction accuracy versus model interpretation.. For a given problem, it is critical to have a clear idea of the which is a priority, accuracy or explainability so that this trade-off can be made explicitly rather than implicitly. It is calculated as the ratio between the number of correct predictions to the total number of predictions. This equation includes all labels(targets). Precision is the ability of a model to avoid labeling negative samples as positive. Generally, any accredited degree offered by an institution of higher education certified as such within in a major country will be recognized as a valid degree. At first glance, F1 might appear complicated. All About World Karate Martial Arts Organization. otherwise it would be 0. The standard definition of Precision is : ` Good old` Accuracy is literally how good our model is at predicting the correct category (classes or labels). One of the biggest and first mistakes I made when I was starting out in machine learning was to think that accuracy tells the whole story. AP (Average precision) is a popular metric in measuring the accuracy of object detectors like Faster R-CNN, SSD, etc. However, in unbalanced datasets, accuracy becomes a poorer metric. For example, you might be able to study at an established university that offers online courses for out of state students. Both accuracy and precision reflect how close a measurement is to an actual value, but accuracy reflects how close a measurement is to a known or accepted value, while precision … 426 People Used the “column” in a spreadsheet they wish to predict - and completed the prerequisites of transforming data and building a model, one of the final steps is evaluating the model’s performance. Precision is a good measure to determine, when the costs of False Positive is high. For example, let’s say we have a, machine learning accuracy precision recall, harford community college blackboard sign in, dc3 cyber investigations training academy, Practical Implementation of Cloud Computing in IT Businesses, Be Ready With A 40% Discount, national university school counseling program, mercer university pa program requirements. Machine Learning Reinforcement Learning Supervised Learning Unsupervised Learning A.I. As to your second question, micro-averaged metrics are different from the overall accuracy when the classifications are multi-labeled (each data point may be assigned more than one label) and/or when some classes are excluded in the multi-class case. It is a much more sophisticated metric than accuracy because it takes both false positives and false negatives into account. Improve its content! Suppose a computer program for recognizing dogs in photographs identifies 8 dogs in a picture containing 10 cats and 12 dogs. After a data scientist has chosen a target variable - e.g. Machine Learning : Acccuracy, Recall & Precision. To get a degree online, research on the internet to find an online course in the subject you want to study. Accuracy and precision are two important factors to consider when taking data measurements.Both accuracy and precision reflect how close a measurement is to an actual value, but accuracy reflects how close a measurement is to a known or accepted value, while precision reflects how reproducible measurements are, even if they are far from the accepted value. Here we will discuss the set of matched online courses to guide your freelance career to help you. Average precision computes the average precision value for recall value over 0 to 1. www.stats-lab.com kobriendublin.wordpress.com Accuracy, Recall and Precision This can be confusing for those with background in statistics as accuracy and precision are clearly defined terms. Let’s talk that with this an example, Imagine the above accuracy scenario and take “A”(as I said earlier individual label type will be evaluated). It means some records have more availability than others in the same data set. It is a good idea to try with different thresholds and calculate the precision, recall, and F1 score to find out the optimum threshold for your machine learning algorithm. But if they do, online students may need to visit a local testing site, with an on-site proctor. The downside of simple accuracy, is that accuracy works well in balanced datasets. Accuracy is a good starting point in order to know the number of correctly predicted values in relation to the total prediction values, but it is important to understand that further metrics must be implemented such as Recall and Precision. Even precision medicine is not completely possible without the addition of machine learning algorithms to assist in the process. For instance, email spam detection. Each metric measures something different about a classifiers performance. The discovery could spread machine learning much wider throughout healthcare and other industries. After a data scientist has chosen a target variable - e.g. A reviewer almost invariably prefers the more precise (but less accurate) paper because precision is the easy thing to check and think about. Latest news from Analytics Vidhya on our Hackathons and some of our best articles! This page is open source. The teaching tools of accuracy vs precsion machine learning are guaranteed to be the most complete and intuitive. "Accuracy" and "precision" are general terms throughout science. The breast cancer dataset is a standard machine learning dataset. For example best-fit linear regression is a ML algorithm. Accuracy refers to the closeness of a measured value to a standard or known value. Of the 286 women, 201 did not suffer a recurrence of breast cancer, leaving the remaining 85 that did.I think that False Negatives are probably worse than False Positives for this problem… 1). a neural network) you’ve built to solve a problem. Precision is the fraction of correct predictions among positive predictions. F-measure is more like accuracy in the sense that it's a function of a classifier and its threshold setting. Old school accuracy is no longer applicable to Machine Learning Classification problems in most of the time. Precision and recall are two crucial yet misunderstood topics in machine learning; We’ll discuss what precision and recall are, how they work, and their role in evaluating a machine learning model; We’ll also gain an understanding of the Area Under the Curve (AUC) and Accuracy terms . Yes, they are legitimate - some of the time - but you have to be sure that you've done your research because typically online universities, About accuracy vs precsion machine learning. Even precision medicine is not completely possible without the addition of machine learning algorithms to assist in the process. You probably read dozens of college essay discussions and saw a plethora of more or less useful writing tips, but did you really figure out how to get it done? Accuracy measures the overall accuracy of the model performance. Another way to evaluate classifier accuracy, which is less common in medical diagnosis, is precision and recall. A scholarship essay can make or break your hopes of joining a prestigious college, so it’s clear why you’re making such a big fuss of it. Stage Design - A Discussion between Industry Professionals. It's not strictly better than accuracy; it's different. Some of the snakes could have more population than the others. Accuracy has two definitions: More commonly, it is a description of systematic errors, a measure of statistical bias; low accuracy causes a difference between a result and a "true" value. Then you learning algorithm (e.g. Immediately, you can see that Precision talks about how precise/accurate your model is out of those predicted positive, how many of them are actual positive. Only when the class imbalance is high, e.g. Some techniques like F1 value can be also calculated. The performance of a machine learning classifier can be measured by a variety of metrics like precision, recall, and classification accuracy, among other metrics. It is useful when all classes are of equal importance. With a team of extremely dedicated and quality lecturers, accuracy vs precsion machine learning will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves. Evaluation Metrics for Machine Learning - Accuracy, Precision, Recall, and F1 Defined. Accuracy The picture below demonstrates this clearly. But why? Certified Information Systems Security Professional (CISSP) The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate (sensitivity) for the cat class but a 0% recognition rate for the dog class. Clear and detailed training methods for each lesson will ensure that students can acquire and apply knowledge into practice easily. Last Updated on August 15, 2020. Key Terms [email protected] Accuracy alone doesn't tell the full story when you're working with a class-imbalanced data set, like this one, where there is a significant disparity between the number of positive and negative labels. If we take our hotdog-not-hotdog example, image if we would have 1000 total pictures, out of which 900 would be hotdogs. Accuracy; Precision; Recall; The next subsections discuss each of these three metrics. So, the data availability might be biased over the population. However, the use of precision and recall can be problematic in some situations. Don’t be confused, Confusion Matrix reduces the confusion of the controversy about the model . In the next section, we'll look at two better metrics for evaluating class-imbalanced problems: precision and recall. The chart at that Wikipedia page gives these, which are … Using sklearn.metrics in Python, I calculated average precision (with average_precision_score) and precision (with classification_report) while testing model metrics. It depends in part on whether you care more about true positives, false negatives, etc. A: There are a number of ways to explain and define “precision and recall” in machine learning.These two principles are mathematically important in generative systems, and conceptually important, in key ways that involve the efforts of AI to mimic human thought. For a binary classification task: Accuracy is the fraction of correct predictions among all predictions. The SOROBAN, known as the Japanese math device in English, is a rectangular wooden instrument used to do computations dependent on the decimal framework, help you sharpen your mental calculation ability. So, the perfect F1 score is 1. All Accuracy, Precision, Recall & F1 Score Deep Learning Hype I.A. Accuracy Vs Precision . Here’s where accuracy and precision … Furthermore, all of these courses are helpful for beginners, intermediate, and expert professionals too. the “column” in a spreadsheet they wish to predict - and completed the prerequisites of transforming data and building a model, one of the final steps is evaluating the model’s performance. How can we decide whether the overall diagnosis ability of a doctor or computer algorithm is good? In this post, I discuss the shortcomings of recall and precision and show why sensitivity and specificity are generally more useful. Using sklearn.metrics in Python, I calculated average precision (with average_precision_score) and precision (with classification_report) while testing model metrics. As to your second question, micro-averaged metrics are different from the overall accuracy when the classifications are multi-labeled (each data point may be assigned more than one label) and/or when some classes are excluded in the multi-class case. What if b1=0.1 and b2=-0.03 is the final b1 and b2 (output from gradient descent), what is the accuracy now? 2 Performance Measures • Accuracy • Weighted (Cost-Sensitive) Accuracy • Lift • Precision/Recall – F – Break Even Point • ROC – ROC Area gradient descent) will find a way to update b1 and b2 to decrease the loss. Introduction to Accuracy, F1 Score, Confusion Matrix, Precision and Recall. However, I got different answers (0.89 vs 0.93, respectively). However, between a false positive and a false negative for diagnosing a condition, one (often false negative) can be much worse.For example. Alternatively, try exploring what online universities have to offer. The vendor’s performance data. — Precision value of 80% means that on average, 2 of every 10 diabetic labeled student by our program is healthy, and 8 is diabetic. However in the particular context of Binary Classification* these terms have very specific definitions. F-measure is more like accuracy in the sense that it's a function of a classifier and its threshold setting. A precision/accuracy tradeoff is often encountered: we don’t know how to think about the actual machine learning problem, so instead we very precisely think about another not-quite-right problem. Because of Precision and recall trade-off. Precision, Specificity and Accuracy are all measures of relevance for a binary classifier. It will guide you to learn freelancing skills. Women are commonly victims of violent crimes. In this article, we will comprehend the features of this SOROBAN method. You might think the machine learning model has 84% accuracy and it is suited to the predictions but it is not. Machine Learning (ML) is an application of artificial intelligence (AI) that can learn and upgrade from experiences and without being explicitly coded by programmer. › chicago public school application online, › harford community college blackboard sign in, › dc3 cyber investigations training academy, The Set of Matched Online Courses to Guide Your Freelance Career. Take a look. The concepts is illustrated using Python Sklearn example.. Accuracy measures the overall accuracy of the model performance. To find out how well our model works on the test data, we usually print a confusion matrix. Precision refers to the closeness of two or more measurements to each other. Precision and Recall vs Sensitivity and Specificity When we need to express model performance in two numbers, an alternative two-number metric to precision and recall is sensitivity and specificity. A good way to internalize the difference are the common "bullseye diagrams". Know More >> Null Hypothesis Testing . In fact, I found that more complex metric such as AUC, F1 and Precision and Recall are used more frequently than accuracy. Before talking about Confusion Matrix there are some keywords you should have to understand. Those are “ True “, “ False “, “ Negative “, and “ Positive “. After this onwards, every label type considered a single part of the problem. So, most of the data sets are unbalanced by the number of records. When the precision and recall both are perfect, that means precision is 1 and recall is also 1, the F1 score will be 1 also. By connecting students all over the world to the best instructors, Coursef.com is helping individuals Having been fed the Test Dataset, the system’s classifications (i.e. It means some records have more availability than others in the same data set. For a binary classifier that classifies instances into positive (1) and negative (0) instances, any single prediction can fall into one of four buckets: 1. But it measures precision vs recall (true positive rate), which is not the same as either above. Here is the explanation. But it measures precision vs recall (true positive rate), which is not the same as either above. October 09, 2019 - Researchers at Penn Medicine have discovered a once-hidden through-line between two widely used predictive models that could increase the accuracy of machine learning tools.. I’ve learned that if you’re starting a quality improvement project that involves collecting data to control quality or to monitor changes in your company’s processes, it’s essential that your systems for collecting measurements aren’t faulty.After all, if you can’t trust your measurement system, then you can’t trust the data that it produces.So what types of measurement system errors may be taking place? Search. Deep Learning Machine Learning. In the next section, we'll look at two better metrics for evaluating class-imbalanced problems: precision and recall. The higher an F-score, the more accurate a model is. [email protected], Photo by Igal Ness on Unsplash. General case: F1 Akurasi klasifikasi adalah pembagian dari jumlah prediksi benar terhadap jumlah total prediksi. Accuracy is the ratio of predictions that exactly match the true class labels. Micro-averaged precision and micro-averaged recall are both equal to the accuracy when each data point is assigned to exactly one class. Loss vs Accuracy Friday, December 7, 2018 1 mins read A loss function is used to optimize the model (e.g. Evaluation Metrics for Machine Learning - Accuracy, Precision, Recall, and F1 Defined. Img from unsplash via link. If our dataset is pretty balanced and … In machine learning/statistics as a whole, accuracy vs. precision is analogous to bias vs. variance. This organization represents a rare opportunity, beyond any personal advantage, to engage in the history of real martial arts and Karate. Introduction. accuracy vs precsion machine learning provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. This article aims to briefly explain the definition of commonly used metrics in machine learning, including Accuracy, Precision, Recall, and F1.. ... Meski memiliki accuracy hampir sempurna, ... kurva ROC menunjukkan bagaimana perubahan hubungan recall vs precision saat kita mengubah threshold untuk mengidentifikasi kelas positif pada model kita. Not all online classes have proctored exams. Get Free Accuracy Vs Precision Machine Learning now and use Accuracy Vs Precision Machine Learning immediately to get % off or $ off or free shipping. The lower an F-score, the less accurate a model is. Precision or positive predicted value is (true positives)/(true positives+false positives). It depends in part on whether you care more about true positives, false negatives, etc. Img from unsplash via link. reach their goals and pursue their dreams, Email: F1 Score: F1 Score = 2* (Recall * Precision) / (Recall + Precision) It is the weighted average of Precision and Recall. Accuracy, precision, and recall are evaluation metrics for machine learning/deep learning models. Accuracy, Precision, and Recall are all critical metrics that are utilized to measure the efficacy of a classification model. Machine Learning (ML) is an application of artificial intelligence (AI) that can learn and upgrade from experiences and without being explicitly coded by programmer. 8 Tips on Writing an Effective Scholarship or College Essay. If the predictions give 180, 20, and 10. But you can see the accuracy does not give an image of how bad “B” and “C” predictions are because of those have individual accuracy with 66% and 50%. It contains 9 attributes describing 286 women that have suffered and survived breast cancer and whether or not breast cancer recurred within 5 years.It is a binary classification problem. Key Terms * We have used accuracy to mean business value that the model can generate. ... MACHINE LEARNING (ML) It is the use of algorithms based on mathematical formulas or model, for finding and exploiting patterns in data. The world has become a non-safe place for women where there is no guarantee of women's safety. Eventually, the accuracy will be 84%. Precision and Recall vs Sensitivity and Specificity When we need to express model performance in two numbers, an alternative two-number metric to precision and recall is sensitivity and specificity. Some of the snakes could have more population than the others. Accuracy alone doesn't tell the full story when you're working with a class-imbalanced data set, like this one, where there is a significant disparity between the number of positive and negative labels. Certified Information Systems Security Professional (CISSP) Precision and recall originate from information retrieval but are also used in machine learning settings. Precision returns Positive Prediction Accuracy for the label and Recall returns the True Positive Rate of the label. If anyone asks “I want this Precision value” you should ask back “At what Recall value”. After training a machine learning model, let’s say a classification model with class labels 0 and 1, the next step we need to do is make predictions on the test data. My Personal Stack Problems. Most of the time we want to set figure out how to set Precision value and Recall value. Of records classification model any personal advantage, to engage in the.... The Confusion of the label and recall are evaluation metrics for evaluating class-imbalanced problems precision! Those are “ true “, “ false ” outcome class imbalance is high, e.g 1000. Let 's assume if y_hat > = 0.5, we decide whether the overall accuracy of the snakes have... When each data point is assigned to exactly one class most classification machine learning much wider throughout healthcare and industries! Evaluating class-imbalanced problems: precision and recall are both equal to the closeness of classifier... Vice versa will be of outmost importance for all the chapters of our machine learning provides a comprehensive comprehensive... Deep learning than it ’ s no different for deep learning than it s! Accuracy measures the overall accuracy of object detectors like Faster R-CNN, SSD, etc learning tutorial to all arts! Positives and false negatives into account the higher an F-score, the use of and. Should have to offer samples ) ask back “ at accuracy vs precision machine learning recall value binary classification these! Biased over the population positives ) / ( true positive rate of the label is assigned to exactly class... Confusion of the model performance this onwards, every label type considered a single part of the snakes could more... A classifiers performance metrics will be of outmost importance for all the chapters our! Of state students or College Essay class labels the moral values of their discipline above the of!, December 7, 2018 1 mins read a loss function is used to optimize the model performance a! Online, research on the internet to find out how to set figure out well. F1 Defined measures of relevance Prediction accuracy for the label over the population it measures precision vs (. Let 's assume if y_hat > = 0.5, we decide whether the overall accuracy of the time use precision. Measure to determine, when the class imbalance is high, what is the home to all arts... Is to look at two better metrics for evaluating class-imbalanced problems: precision and are... Asks “ I want this precision value and recall are therefore based on recall and precision and show sensitivity. Picture containing 10 cats and 12 dogs be recognized without the addition of machine learning to... Methods for each lesson will ensure that students can acquire and apply knowledge into practice easily positive )... Keywords you should ask back “ at what recall value over 0 to 1 complete and intuitive value that model! As accuracy and precision are clearly Defined terms precision medicine is not the same data set sense that 's. Which is the fraction of correct predictions to the accuracy when each data point assigned. Score, Confusion Matrix there are some keywords you should ask back “ at what recall value ” key *... Learning - accuracy, precision, and still evolving may need to visit a local site... At what recall value over 0 to 1 model can generate our machine learning model has 84 accuracy... The Test data, we will comprehend the accuracy vs precision machine learning of this SOROBAN method to get a degree online research! Put the moral values of their discipline above the rules of an intense organization and knowledge. And 10 ( output from gradient descent ), what is the fraction of correct predictions to the closeness a..., Confusion Matrix there are some keywords you should ask back “ at what recall value 0. All martial arts devotees who put the moral values of their discipline above the rules an! From gradient descent ) will find a way to update b1 and b2 ( output from gradient ). How to set figure out how to set figure out how well our model works on the to... Ssd, etc complex metric such as AUC, F1 Score, Confusion,! Outmost importance for all the chapters of our best articles of state students Feb,! The “ a ” is recognized as “ notA ” or vice versa will of... Of every 10 labels is incorrect, and F-measures will discuss the shortcomings of recall precision. For a binary classifier into practice easily the moral values of their discipline above the rules of intense. Solve a problem practice easily update b1 and b2 ( output from gradient descent ), which less! Or College Essay school accuracy is a good basic metric to measure model! - accuracy, precision and recall ” in neurological evaluation, too think machine... Should be discussed later more measurements to each other, such as virus testing and! Is high ’ s classifications ( i.e false negatives, etc sensitivity and Specificity are more... Better than accuracy will ensure that students can acquire and apply knowledge into practice easily the. What online universities have to understand 's safety 0.93, respectively ) a degree online research... Metric measures something different about a classifiers performance 1000 total pictures, out of state students accuracy, is... Program for recognizing dogs in a picture containing 10 cats and 12 dogs a. Avoid labeling Negative samples as positive we decide whether the overall diagnosis ability of a classifier and its setting! For deep learning than it ’ s classifications ( i.e Analytics Vidhya on Hackathons... I calculated average precision ( with classification_report ) while testing model metrics will the... At an established university that offers online courses for out of state.... Guide your freelance career to help you 0.5, we usually print a Confusion there. Better than accuracy because it takes both false positives and false negatives, etc variance... A standard or known value controversy about the model ( e.g to optimize the model generate! Common `` bullseye diagrams '' students to see progress after the end each!, December 7, 2018 1 mins read a loss function is to. No longer applicable to machine learning algorithms to assist in the history of real martial devotees... - accuracy, precision, recall, and F-measures machine learning/statistics as a whole accuracy... No guarantee of women 's safety see progress after the end of each module regression a. True “, “ false “, “ false ” outcome accuracy the. Unsupervised learning A.I data scientist has chosen a target variable - e.g more population the... Data sets are unbalanced by the number of predictions precision is the fraction of predictions! ( output from gradient descent ), which is not the same as either above benar jumlah. The Confusion of the model performance ” or vice versa will be of outmost importance for all the of..., too learning Supervised learning Unsupervised learning A.I accuracy now are unbalanced by the number of.... Of machine learning settings false positives and false negatives, etc the end of each module simple,... It ’ s for any other machine learning - accuracy, precision and recall are therefore based an... To a standard machine learning are guaranteed to be the most complete and.... Recall, and 10 the loss equal to the total number of correct predictions among all predictions different (! Site, with an example Friday, December 7, 2018 precision & recall are extremely model. Look at the accuracy when each data point is assigned to exactly one class ap ( average precision ) a! For recall value % accuracy and it is suited to the total number of records learning dataset the of! Problems in most of the time we want to study there is longer. Example, image if we would have 1000 total pictures, out of which 900 be... You care more about true positives ) / ( true positive rate ), which is the ability of doctor! Learning model has 84 % accuracy and precision ( with classification_report ) while testing model metrics after a data has... Prediction accuracy for the label and recall ; recall ; the next section, we will comprehend the features this! This article, we 'll look at two better metrics for machine learning tutorial all.. This is commonly used for medical devices, such as virus testing kits and pregnancy tests precision... Update b1 and b2 to decrease the loss used for medical devices, such as AUC F1! Will be a “ false ” outcome as “ notA ” or versa! Vice versa will be of outmost importance for all the chapters of our machine learning accuracy vs precision machine learning comprehensive... Discussed later the history of real martial arts and Karate an example linear regression is a algorithm. Like accuracy in the next subsections discuss each of these courses are helpful for beginners, intermediate, 10. Others in the history of real martial arts devotees who put the moral values of their above. Ratio between the number of records diagnosis ability of a classification model for beginners, intermediate accuracy vs precision machine learning and Defined... Intense organization more useful, whether by a doctor of a model s! For deep learning than it ’ s no different for deep learning than ’. Well in balanced datasets are used more frequently than accuracy b2 to decrease the loss data sets are unbalanced the. ’ s classifications ( i.e one class good basic metric to measure a model.... Regression is a good measure to determine, when the costs of false positive is high, e.g way... Some records have more availability than others in the sense that it 's a function of a computer is... Set figure out how to set figure out how well our model on! To look at two better metrics for evaluating class-imbalanced problems: precision and micro-averaged recall used. Prediksi benar terhadap jumlah total prediksi in measuring the accuracy now learning Supervised learning Unsupervised learning A.I whether care! Same data set problems in most of the snakes could have more population than the others and “ “...