Discussion on Greek and Latin elements in English words

Greek and Latin elements in English words
Quiz 7 is on tomorrow, only one attempt and 30 minutes to finish it. It will available on tomorrow at 11am

 

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  1. Choose the area of your preference, whatever you would like to describe in a dataset and explain using data mining. For example: actresses/actors, food, movies, sports, music bands, or anything you want.

Create a data file in .arff format containing about 20 entries, each described by

about 4 attributes, with the last attribute containing your preference (class attribute), e.g.

 

@relation food

@attribute calories numeric

@attribute taste {sweet, sour, bitter, salty}

@attribute course {appetizer, main, dessert, drink}

@attribute vegetarian {yes, no}

@attribute like_it {yes, no}

@data

100, sweet, dessert, yes, yes%icecream

80, bitter, drink, yes, yes%beer

2, sweet, dessert,yes, no%cake

 

Compare 3 algorithms for classification of your data: decision trees, a classification or an association rule learner, and naive Bayes. For each algorithm check what the error is (which algorithm can explain your personal liking the best), and observe the generated rules (do they tell you anything interesting?).

 

 

 

 

  1. Use the following learning schemes to compare the training set and 10-fold stratified cross-validation scores of the labor data (in labor_neg_nominal.arff):

 

  • k-nearest neighbours (IBk) with decision trees (j48.J48)
  • k-nearest neighbours (IBk) with decision trees j48.J48 with option -M 3, so that each leaf has at least 3 instances.

 

  1. A) What does the training set evaluation score tell you? B) What does the cross-validation score evaluate?
  2. C) Which one of these models would you say is the best? Why?

 

 

 

  1. Use the following learning schemes to analyze the Titanic data (in titanic.arff).

C4.5                             – weka.classifiers.j48.J48

Association rules         -weka.associations.apriori

Decision List                – weka. Classifiers.PART

 

  1. A) What is the most important descriptor (attribute) in titanic.arff?
  2. B) How well were these methods able to learn the patterns in the dataset? Quantify your

answer?

  1. C) Compare the training set and 10-fold cross-validations scores of the methods.
  2. D) Would you trust these models? Did they really learn what was important to survive the

Titanic disaster?

  1. E) Which one would you trust more, even if just very slightly? Why?

 

 

 

 

  1. Choose one of the following three files: soybean.arff, autoprice.arff, hungarian, zoo.arff or zoo2_x.arff and use any two schemas of your choice to build and compare the models. Which one of the models would you keep? Why?

 

 

 

 

 

 

 

 

 

  1. Use the Association rule learner APRIORI method to find the association rule in the Weather.nominal data set. How many rules did it produce?  How large are the item sets? What was the largest one? What happened when you increased/decreased the confidence level? What about the number of rules?  What happens when you increase the confidence parameter to 2?  Why?

 

Wilfrid Laurier University Department of Geography and Environmental Studies
GG101 Introduction to Physical Geography
Lab 6: Environmental Change with focus on Climate
Tasks and Questions
Historical Record Figure 1 in the Lab 6 background reading shows the global mean annual temperature record from 1880 to 2019 presented as departures from the 1951-80 mean. Figure 3 shows the change in Canada over the period 1948 to 2019 expressed as departures over the period 1961-90. Figure 3 was from the Climate Trends and Variations Bulletin (Annual Report 2019) published by Environment Canada. The graph below has been generated from that same data and adjusted so the baseline period is also 1951-80. The years plotted are 1950 to 2019. The annual values are shown in grey, the red line is a 5 year running average and the blue line is a linear trend fit over the period 1970 to 2019.
Task 1 Compare Figure 1 from the background reading with the graph above. From Figure 1, consider the period only from 1950 to 2019. Both records show that temperatures in the 1950s and 1960s were largely unchanged (flat). By the mid-1970’s the temperatures began to increase.
Question 1 (2 marks)
For the period from 1970 to 2019.
a) Estimate the magnitude of the temperature change that occurred Globally and in Canada,
present your estimates as numeric values over that interval.
b) Comment on the year to year variability (variation) in the two records, which record has more
variability.
Spatial Pattern over the Period 2000-2019 In the Climate Atlas of Canada, a base period of 1976 to 2005 is used to capture ‘current conditions’. Go the GISS global mapping site described on pages 2, 3 and 4 in the background reading. Set the
Data Sources using the parameters shown in the background reading (they are the default data sources). The Map Type will be Anomalies. For the Base Period use 1976 to 2005. For the Time
Interval use 2000 to 2019 (20 year time interval). We will examine mapping output that shows the spatial variation in global temperature over the period 2002 to 2019 (relative to 1976 to 2005). For the Mean Period use: a) Annual (Dec-Nov) b) NH Winter (Dec-Feb) c) NH Spring (Mar-May) d) NH Summer (Jun-Aug) e) NH Fall (Sep-Nov)
Task 2 (5 marks)
Examine the five maps you have created that show the spatial distributions of the changes in mean
Annual, Winter, Spring, Summer, and Fall temperatures over the period 2000 to 2019. Complete
Table 1 by identifying the global areas that are experiencing the most substantial warming and
cooling for the selected time periods. You may identify global areas in a number of ways, such as
latitudinal zone (e.g., high latitude land areas of North America), segment of a continent (e.g.,
northeastern Africa), country (e.g., Chile and Peru), or segment of an ocean (e.g., Arctic Ocean
north of Siberia).
Question 2 (1 mark)
What season has shown the most significant change in temperature over the period 2000 to 2019?
Question 3 (1 mark)
Overall, what latitudinal zone has experienced the greatest warming since 2000?
Table 1. Spatial patterns in warming and cooling over the period 2000 to 2019.
Time
Period
Global Locations
Areas of Highest Warming Areas Cooling
Annual
Winter
Spring
Summer
Fall
Task 3 The data in the maps from GISS show us the magnitude and spatial pattern of warming that has occurred over the last 20 years. We will use results from a General Circulation Model (GCM) from the same organization to examine the distribution of changes to mean annual temperature that would occur if the concentration of greenhouse gases in the atmosphere increased to the equivalent of 520 ppm CO2 and a new planetary energy balance were to be established. The CGM is called ROCKE-3D and the URL is in the background document. Examine the global pattern of surface temperature change that is projected under a scenario of a doubling of CO2 (2X CO2) as compared to pre-industrial CO2 levels. Repeat the process for a four times CO2 (4X CO2) scenario. For these two maps use the Variable called surface temperature.
Question 4 (1 mark)
What is the average warming that would occur for the globe as a whole for the two scenarios?
2X CO2 :
4X CO2 :
Task 4 We will now use the ROCKE-3D model to examine how a series of variable may change as we move to a higher CO2 world. For these analyses we will use two time periods, they are the Pre-Industrial and the 4X CO2 time frame. We will examine three Variables, they are: a) sea ice fraction (%) b) planetary albedo (%) c) cloud cover (%) For each of these variables examine the results from the pre-industrial model and the 4X CO2 model.
Question 5 (3 marks)
Describe the overall changes (global) that occur when we compare the pre-industrial to the 4X CO2
model for these three variables.
Question 6 (1 mark)
Why is a loss of sea ice in polar regions associated with a lower albedo in the same regions.
Task 5 (5 marks) In this section we will look at climate changes at the local scale that are projected for the Toronto area under two different scenarios. Go to the Climate Atlas of Canada interactive mapping website. In the Climate Atlas interactive map, examine the Annual, Winter, Spring, Summer, and Fall mean temperatures for the Toronto location. Record the average values for each of the above variables for 1976-2005, and for the projected time periods 2021-2050 and 2051-2080 under the Less Climate Change and More Climate Change scenarios. Enter the data into Table 2.
Table 2. Average temperature values for Toronto, with projected average temperatures under the Less and More Climate Change scenarios.
Variable 1976-2005
2021-2050 2051-2080
Less More Less More
Annual
Winter
Spring
Summer
Fall Examine the graphing output for the above variables for the two scenarios (low and high amounts of climate change). The high amount of climate change corresponds to a high emission scenario, this is similar to the pathway that we are currently following. The magnitude of change for each of the above variables is larger for the higher emission case. Figure 11.27 in your textbook shows the global projections of mean temperature change for these same two scenarios, they are called RCP8.5 and RCP4.5 for the high and low emissions cases. Refer to the graphs from the Climate Atlas that you have just examined and Figure 11.27.
Question 7 (2 marks)
Compare the projected temperature changes for the Toronto area that will occur under the two
scenarios. Comment on the seasonal pattern and on the difference between the two scenarios.
Question 8 (4 marks)
Return to Climate Atlas mapping site, zoom down to the Toronto area. There are several variables
that are listed under the Heading “Hot Weather”. Using the higher emission scenario for the two
projected time periods examine the variables: (i) Very Hot Days, (ii) Tropical Nights, (iii) Number of
Heat Waves, and (iv) Average Length of Heat Waves. What do the projections show? Using the
resources available from the Climate Atlas site comment on why these projected changes are of
particular concern for urban areas in Canada.

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