Let’s take an example where each data point contains only one input feature. One means high similarity(the data objects are very similar). It is often expressed as a number between zero and one by conversion: zero means low similarity(the data objects are dissimilar). The similarity measure is usually expressed as a numerical value: It gets higher when the data samples are more alike. Another example is when we talk about dissimilar outliers compared to other data samples(e.g., anomaly detection). KNN), where the data objects are labeled based on the features’ similarity. All other data samples are grouped into different ones. Moreover, these terms are often used in clustering when similar data samples are grouped into one cluster. On the other hand, the dissimilarity measure is to tell how much the data objects are distinct. In data science, the similarity measure is a way of measuring how data samples are related or closed to each other. Illustrations and equations were generated using tools like Matplotlib, Tex, Scipy, Numpy and edited using GIMP. Quick note: Everything written and visualized has been created by the author unless it was specified. “There is no Royal Road to Geometry.” - Euclid Compare the types of survivorship curves you would expect to find in human populations in industrialized countries with good health care versus African countries with a high mortality rate from HIV and other diseases due to poverty.Various ML metrics. Describe the effect of adding a second cause of death to the survivorship curve of Population 2 3. What type of survivorship curve is seen in Population 1? 2. NOTE: Graph points for Population 1 using dots and Population 2 using X's. Population 1 Population 2 (death from predation) (death from predation and disease) Number Percentage Number Percentage surviving surviving surviving surviving 20 10096 20 100% Generation 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 population (percentage) surviving 100 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 generation Figure 13.10: Percentage survivorship for two populations. Table 13.3: Number of survivors for each generation. Graph your results in Figure 13.10 using x's for your data points for Population 2. Record your data under Population 2 in Table 13.3. Repeat the above procedure except this time all the #1 dice die from predation and all the #2 dice die from disease 2. Graph the results in Figure 13.10 using dots for your data points. Take the surviving dice and repeat steps 1 - 5 for each successive generation until all the dice have died and have been set aside. Calculate the percentage of survivors using the following formula and record as the "Percentage Surviving": percentage surviving = number surviving X 100 20 6. Count the number of surviving dice and record that number in Table 13.3 as Generation 1, Population 1 "Number Surviving 5. All dice that land with the #1 on the up face will die due to predation. Put the dice in the cup, shake the cup and empty onto the tray. Population Survivorship Activity Materials Examples include fish, reptiles, and insects Type 1 Type 11 log number of survivors Type III birth death age (percent of lifespan) Figure 13.9: Three basic types of survivorship curves. ![]() Type II-the mortality rate for the young is very high few individuals reach oid age: typically type Ill organisms have a very high birth rate and provide little or no protections to their young. Some birds follow this pattern of survival. Type - the mortality rate is relatively constant regardless of age. ![]() Examples Include humans and large mammals. ![]() There are three basic types of survivorship curves (see Figure 13.9): Type ! - low mortality rate in the young most individuals die in old age: typically type organisms have a low birth rate and nurture and protect their young. Transcribed image text: SURVIVORSHIP CURVES Survivorship curves illustrate the percentage of individuals in a population that survive over the average life span of that species and provide information regarding the age structure of the population.
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