Classification¶
Classification is a supervised learning task, in which the training data we feed to the algorithm includes the desired labels. The aim of classification task is to classify each data into the corresponding class. So we have to use dataset with known labels to train a classification model. Then choose one model which has best performance to predict unknown data.
Note：If your task is binary classification, the label must be set to either 0 or 1. All metric values would be calculated from the label 1 by default, such as precision, accurary and so on.
Table of Contents¶
1. Train-Test Data Preparation¶
After installing it, the first step is to run the Geochemistry Pi framework in your terminal application.
In this section, we take the built-in dataset as an example by running:
geochemistrypi data-mining
Alternatively, it is perfectly fine if you would like to use your own dataset like:
geochemistrypi data-mining --data your_own_data_set.xlsx
You can choose the appropriate option based on the program’s prompts and press the Enter key to select the default option (inside the parentheses).
Welcome to Geochemistry π!
Initializing...
No Training Data File Provided!
Built-in Data Loading.
No Application Data File Provided!
Built-in Application Data Loading.
✨ Press Ctrl + C to exit our software at any time.
✨ Input Template [Option1/Option2] (Default Value): Input Value
✨ Use Previous Experiment [y/n] (n):
✨ New Experiment (GeoPi - Rock Classification):
'GeoPi - Rock Classification' is activated.
✨ Run Name (XGBoost Algorithm - Test 1):
(Press Enter key to move forward.)
After pressing the Enter key, the program propts the following options to let you **choose the Built-in Training Data**:
-*-*- Built-in Training Data Option-*-*-
1 - Data For Regression
2 - Data For Classification
3 - Data For Clustering
4 - Data For Dimensional Reduction
(User) ➜ @Number: 2
Here, we choose *2 - Data For Classification* and press the Enter key to move forward.
Now, you should see the output below on your screen:
Successfully loading the built-in training data set
'Data_Classification.xlsx'.
--------------------
Index - Column Name
1 - CITATION
2 - SAMPLE NAME
3 - Label
4 - Notes
5 - LATITUDE
6 - LONGITUDE
7 - Unnamed: 6
8 - SIO2(WT%)
9 - TIO2(WT%)
10 - AL2O3(WT%)
11 - CR2O3(WT%)
12 - FEOT(WT%)
13 - CAO(WT%)
14 - MGO(WT%)
15 - MNO(WT%)
16 - NA2O(WT%)
--------------------
(Press Enter key to move forward.)
We hit Enter key to keep moving.
Then, we choose *2 - Data For Classification* as our **Built-in Application Data**:
-*-*- Built-in Application Data Option-*-*-
1 - Data For Regression
2 - Data For Classification
3 - Data For Clustering
4 - Data For Dimensional Reduction
(User) ➜ @Number: 2
After this, the program will display a list for Column Name:
Successfully loading the built-in inference data set
'InferenceData_Classification.xlsx'.
--------------------
Index - Column Name
1 - CITATION
2 - SAMPLE NAME
3 - Label
4 - Notes
5 - LATITUDE
6 - LONGITUDE
7 - Unnamed: 6
8 - SIO2(WT%)
9 - TIO2(WT%)
10 - AL2O3(WT%)
11 - CR2O3(WT%)
12 - FEOT(WT%)
13 - CAO(WT%)
14 - MGO(WT%)
15 - MNO(WT%)
16 - NA2O(WT%)
17 - Unnamed: 16
18 - SC(PPM)
19 - TI(PPM)
20 - V(PPM)
21 - CR(PPM)
22 - NI(PPM)
23 - RB(PPM)
24 - SR(PPM)
25 - Y(PPM)
26 - ZR(PPM)
27 - NB(PPM)
28 - BA(PPM)
29 - LA(PPM)
30 - CE(PPM)
31 - PR(PPM)
32 - ND(PPM)
33 - SM(PPM)
34 - EU(PPM)
35 - GD(PPM)
36 - TB(PPM)
37 - DY(PPM)
38 - HO(PPM)
39 - ER(PPM)
40 - TM(PPM)
41 - YB(PPM)
42 - LU(PPM)
43 - HF(PPM)
44 - TA(PPM)
45 - PB(PPM)
46 - TH(PPM)
47 - U(PPM)
--------------------
(Press Enter key to move forward.)
After pressing the Enter key, you can choose whether you need a world map projection for a specific element option:
-*-*- World Map Projection -*-*-
World Map Projection for A Specific Element Option:
1 - Yes
2 - No
(Plot) ➜ @Number:
More information of the map projection can be found in the section of World Map Projection. In this tutorial, we skip it by typing 2 and pressing the Enter key.
Based on the output prompted, we include column 3 (Label) because it represents the classification label. In a classification task, our goal is to predict or classify data points into specific categories or classes, and the “Label” column contains the information that we want to predict or classify. Then, we also include column 8, 9, 10, 11, 12, 13 (i.e. [8, 13]) in our example.
-*-*- Data Selection -*-*-
--------------------
Index - Column Name
1 - CITATION
2 - SAMPLE NAME
3 - Label
4 - Notes
5 - LATITUDE
6 - LONGITUDE
7 - Unnamed: 6
8 - SIO2(WT%)
9 - TIO2(WT%)
10 - AL2O3(WT%)
11 - CR2O3(WT%)
12 - FEOT(WT%)
13 - CAO(WT%)
14 - MGO(WT%)
15 - MNO(WT%)
16 - NA2O(WT%)
--------------------
Select the data range you want to process.
Input format:
Format 1: "[**, **]; **; [**, **]", such as "[1, 3]; 7; [10, 13]" --> you want to deal with the columns 1, 2, 3, 7, 10, 11, 12, 13
Format 2: "xx", such as "7" --> you want to deal with the columns 7
@input: 3; [8, 13]
Have a double-check on your selection and press Enter to move forward:
--------------------
Index - Column Name
3 - Label
8 - SIO2(WT%)
9 - TIO2(WT%)
10 - AL2O3(WT%)
11 - CR2O3(WT%)
12 - FEOT(WT%)
13 - CAO(WT%)
--------------------
(Press Enter key to move forward.)
> Enter
The Selected Data Set:
Label SIO2(WT%) ... FEOT(WT%) CAO(WT%)
0 1 53.640000 ... 11.130000 20.240000
1 1 52.740000 ... 12.140000 20.480000
2 1 51.710000 ... 6.850000 22.420000
3 1 50.870000 ... 7.530000 22.450000
4 1 50.920000 ... 6.930000 22.620000
... ... ... ... ... ...
2006 0 52.628866 ... 2.202400 21.172240
2007 0 52.535656 ... 2.093113 21.150105
2008 0 52.163411 ... 2.202465 21.600643
2009 0 44.940000 ... 6.910000 22.520000
2010 0 46.750000 ... 7.550000 22.540000
[2011 rows x 7 columns]
(Press Enter key to move forward.)
> Enter
Now, you should see
-*-*- Basic Statistical Information -*-*-
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2011 entries, 0 to 2010
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Label 2011 non-null int64
1 SIO2(WT%) 2010 non-null float64
2 TIO2(WT%) 2010 non-null float64
3 AL2O3(WT%) 2010 non-null float64
4 CR2O3(WT%) 2011 non-null float64
5 FEOT(WT%) 2011 non-null float64
6 CAO(WT%) 2011 non-null float64
dtypes: float64(6), int64(1)
memory usage: 110.1 KB
None
Some basic statistic information of the designated data set:
Label SIO2(WT%) ... FEOT(WT%) CAO(WT%)
count 2011.00000 2010.000000 ... 2011.000000 2011.000000
mean 0.73446 52.110238 ... 3.215889 21.442025
std 0.44173 2.113287 ... 1.496576 2.325046
min 0.00000 0.218000 ... 1.281000 0.097000
25% 0.00000 51.350135 ... 2.535429 20.532909
50% 1.00000 52.200000 ... 2.920000 21.600000
75% 1.00000 52.980000 ... 3.334500 22.421935
max 1.00000 56.301066 ... 18.270000 26.090000
[8 rows x 7 columns]
Successfully calculate the pair-wise correlation coefficient among
the selected columns.
Save figure 'Correlation Plot' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test
1/artifacts/image/statistic.
Successfully...
Successfully...
...
Successfully store 'Data Selected' in 'Data Selected.xlsx' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
(Press Enter key to move forward.)
You should now see a lot of output on your screen, but don’t panic.
This output just provides basic statistical information about the dataset, including count, mean, standard deviation, and percentiles for the data column labeled “Label.” It also documents the successful execution of tasks such as calculating correlations, drawing distribution plots, and saving generated charts and data files.
Now, let’s press the Enter key to proceed.
-*-*- Missing Value Check -*-*-
Check which column has null values:
--------------------
Label False
SIO2(WT%) True
TIO2(WT%) True
AL2O3(WT%) True
CR2O3(WT%) False
FEOT(WT%) False
CAO(WT%) False
dtype: bool
--------------------
The ratio of the null values in each column:
--------------------
SIO2(WT%) 0.000497
TIO2(WT%) 0.000497
AL2O3(WT%) 0.000497
Label 0.000000
CR2O3(WT%) 0.000000
FEOT(WT%) 0.000000
CAO(WT%) 0.000000
dtype: float64
--------------------
Note: you'd better use imputation techniques to deal with the
missing values.
(Press Enter key to move forward.)
> Enter
2. Missing Value Processing¶
Now, the program will ask us if we want to deal with the missing values, we can choose yes here:
-*-*- Missing Values Process -*-*-
Do you want to deal with the missing values?
1 - Yes
2 - No
(Data) ➜ @Number: 1
> Enter
For strategy, we choose 2 - Impute Missing Values:
-*-*- Strategy for Missing Values -*-*-
1 - Drop Rows with Missing Values
2 - Impute Missing Values
Notice: Drop the rows with missing values may lead to a
significant loss of data if too many features are chosen.
Which strategy do you want to apply?
(Data) ➜ @Number: 2
> Enter
Based on the propt, we choose the 1 - Mean Value in this example and the input data be processed automatically as:
-*-*- Imputation Method Option -*-*-
1 - Mean Value
2 - Median Value
3 - Most Frequent Value
4 - Constant(Specified Value)
Which method do you want to apply?
(Data) ➜ @Number: 1
> Enter
Successfully fill the missing values with the mean value of each
feature column respectively.
(Press Enter key to move forward.)
> Enter
-*-*- Hypothesis Testing on Imputation Method -*-*-
Null Hypothesis: The distributions of the data set before and
after imputing remain the same.
Thoughts: Check which column rejects null hypothesis.
Statistics Test Method: Kruskal Test
Significance Level: 0.05
The number of iterations of Monte Carlo simulation: 100
The size of the sample for each iteration (half of the whole data
set): 1005
Average p-value:
Label 1.0
SIO2(WT%) 0.9993660077630827
TIO2(WT%) 0.9966146379846705
AL2O3(WT%) 0.9981857963077964
CR2O3(WT%) 1.0
FEOT(WT%) 1.0
CAO(WT%) 1.0
Note: 'p-value < 0.05' means imputation method doesn't apply to
that column.
The columns which rejects null hypothesis: None
Successfully draw the respective probability plot (origin vs.
impute) of the selected columns
Save figure 'Probability Plot' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test
1/artifacts/image/statistic.
Successfully store 'Probability Plot' in 'Probability Plot.xlsx'
in /Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test
1/artifacts/image/statistic.
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2011 entries, 0 to 2010
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Label 2011 non-null float64
1 SIO2(WT%) 2011 non-null float64
2 TIO2(WT%) 2011 non-null float64
3 AL2O3(WT%) 2011 non-null float64
4 CR2O3(WT%) 2011 non-null float64
5 FEOT(WT%) 2011 non-null float64
6 CAO(WT%) 2011 non-null float64
dtypes: float64(7)
memory usage: 110.1 KB
None
Some basic statistic information of the designated data set:
Label SIO2(WT%) ... FEOT(WT%) CAO(WT%)
count 2011.00000 2011.000000 ... 2011.000000 2011.000000
mean 0.73446 52.110238 ... 3.215889 21.442025
std 0.44173 2.112761 ... 1.496576 2.325046
min 0.00000 0.218000 ... 1.281000 0.097000
25% 0.00000 51.350271 ... 2.535429 20.532909
50% 1.00000 52.200000 ... 2.920000 21.600000
75% 1.00000 52.980000 ... 3.334500 22.421935
max 1.00000 56.301066 ... 18.270000 26.090000
[8 rows x 7 columns]
Successfully store 'Data Selected Dropped-Imputed' in 'Data
Selected Dropped-Imputed.xlsx' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
(Press Enter key to move forward.)
> Enter
The next step is to select your feature engineering options, for simplicity, we omit the specific operations here. For detailed instructions, please see here.
-*-*- Feature Engineering -*-*-
The Selected Data Set:
--------------------
Index - Column Name
1 - Label
2 - SIO2(WT%)
3 - TIO2(WT%)
4 - AL2O3(WT%)
5 - CR2O3(WT%)
6 - FEOT(WT%)
7 - CAO(WT%)
--------------------
Feature Engineering Option:
1 - Yes
2 - No
(Data) ➜ @Number: 2
> Enter
Successfully store 'Data Selected Dropped-Imputed
Feature-Engineering' in 'Data Selected Dropped-Imputed
Feature-Engineering.xlsx' in /Users/lcthw/geopi/geopi_output/GeoPi
- Rock Classification/XGBoost Algorithm - Test 1/artifacts/data.
(Press Enter key to move forward.)
> Enter
3. Data Processing¶
We select 2 - Classification as our model:
-*-*- Mode Selection -*-*-
1 - Regression
2 - Classification
3 - Clustering
4 - Dimensional Reduction
(Model) ➜ @Number: 2
> Enter
(Press Enter key to move forward.)
> Enter
Before we start the classfication model training, we have to specify our X and Y data set. in the example of our selected data set, we take column [2,7] as our X set and column 1 as Y.
-*-*- Data Segmentation - X Set and Y Set -*-*-
Divide the processing data set into X (feature value) and Y
(target value) respectively.
Selected sub data set to create X data set:
--------------------
Index - Column Name
1 - Label
2 - SIO2(WT%)
3 - TIO2(WT%)
4 - AL2O3(WT%)
5 - CR2O3(WT%)
6 - FEOT(WT%)
7 - CAO(WT%)
--------------------
The selected X data set:
Select the data range you want to process.
Input format:
Format 1: "[**, **]; **; [**, **]", such as "[1, 3]; 7; [10, 13]" --> you want to deal with the columns 1, 2, 3, 7, 10, 11, 12, 13
Format 2: "xx", such as "7" --> you want to deal with the columns 7
@input: [2, 7]
--------------------
Index - Column Name
2 - SIO2(WT%)
3 - TIO2(WT%)
4 - AL2O3(WT%)
5 - CR2O3(WT%)
6 - FEOT(WT%)
7 - CAO(WT%)
--------------------
Successfully create X data set.
The Selected Data Set:
SIO2(WT%) TIO2(WT%) ... FEOT(WT%) CAO(WT%)
0 53.640000 0.400000 ... 11.130000 20.240000
1 52.740000 0.386000 ... 12.140000 20.480000
2 51.710000 0.730000 ... 6.850000 22.420000
3 50.870000 0.780000 ... 7.530000 22.450000
4 50.920000 0.710000 ... 6.930000 22.620000
... ... ... ... ... ...
2006 52.628866 0.409385 ... 2.202400 21.172240
2007 52.535656 0.422012 ... 2.093113 21.150105
2008 52.163411 0.665545 ... 2.202465 21.600643
2009 44.940000 3.930000 ... 6.910000 22.520000
2010 46.750000 3.360000 ... 7.550000 22.540000
[2011 rows x 6 columns]
Basic Statistical Information:
Some basic statistic information of the designated data set:
SIO2(WT%) TIO2(WT%) ... FEOT(WT%) CAO(WT%)
count 2011.000000 2011.000000 ... 2011.000000 2011.000000
mean 52.110238 0.411301 ... 3.215889 21.442025
std 2.112761 0.437225 ... 1.496576 2.325046
min 0.218000 0.000000 ... 1.281000 0.097000
25% 51.350271 0.166500 ... 2.535429 20.532909
50% 52.200000 0.320000 ... 2.920000 21.600000
75% 52.980000 0.511400 ... 3.334500 22.421935
max 56.301066 6.970000 ... 18.270000 26.090000
[8 rows x 6 columns]
Successfully store 'X Without Scaling' in 'X Without Scaling.xlsx'
in /Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
(Press Enter key to move forward.)
> Enter
Now, we select our Y data:
-*-*- Data Segmentation - X Set and Y Set-*-*-
Selected sub data set to create Y data set:
--------------------
Index - Column Name
1 - Label
2 - SIO2(WT%)
3 - TIO2(WT%)
4 - AL2O3(WT%)
5 - CR2O3(WT%)
6 - FEOT(WT%)
7 - CAO(WT%)
--------------------
The selected Y data set:
Notice: Normally, please choose only one column to be tag column
Y, not multiple columns.
Notice: For classification model training, please choose the label
column which has distinctive integers.
Select the data range you want to process.
Input format:
Format 1: "[**, **]; **; [**, **]", such as "[1, 3]; 7; [10, 13]" --> you want to deal with the columns 1, 2, 3, 7, 10, 11, 12, 13
Format 2: "xx", such as "7" --> you want to deal with the columns 7
@input: 1
> Enter
--------------------
Index - Column Name
1 - Label
--------------------
Successfully create Y data set.
The Selected Data Set:
Label
0 1.0
1 1.0
2 1.0
3 1.0
4 1.0
... ...
2006 0.0
2007 0.0
2008 0.0
2009 0.0
2010 0.0
[2011 rows x 1 columns]
Basic Statistical Information:
Some basic statistic information of the designated data set:
Label
count 2011.00000
mean 0.73446
std 0.44173
min 0.00000
25% 0.00000
50% 1.00000
75% 1.00000
max 1.00000
Successfully store 'Y' in 'Y.xlsx' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
(Press Enter key to move forward.)
> Enter
In the following steps, you can choose whether to perform feature scaling and feature selection on your data according to your needs.
-*-*- Feature Scaling on X Set -*-*-
1 - Yes
2 - No
(Data) ➜ @Number:
-*-*- Feature Selection on X set -*-*-
1 - Yes
2 - No
(Data) ➜ @Number:
After conducting the two steps above, we come to the point of Data Splitting, in this step, the program is suggesting how to split your dataset into a training set and a test set. Typically, it is recommended to reserve 20% of your entire dataset as the test set. In other words, you should allocate 20% of your data for testing the performance of your model, while the remaining 80% will be used for training the model.
-*-*- Data Split - Train Set and Test Set -*-*-
Notice: Normally, set 20% of the dataset aside as test set, such
as 0.2.
(Data) ➜ @Test Ratio: 0.2
> Enter
-------------------------
The Selected Data Set: X Train
SIO2(WT%) TIO2(WT%) ... FEOT(WT%) CAO(WT%)
261 54.70400 0.021000 ... 1.595000 24.297000
607 50.51000 0.870000 ... 4.890000 18.940000
1965 53.32721 0.600492 ... 2.076389 21.331999
240 55.09900 0.112000 ... 1.918000 24.274000
819 50.48000 0.490000 ... 4.060000 19.980000
... ... ... ... ... ...
1130 54.36500 0.053000 ... 2.760000 23.702000
1294 51.17100 0.244000 ... 3.349000 22.085000
860 52.50000 0.360000 ... 3.710000 18.140000
1459 49.04400 0.821000 ... 2.953000 21.859000
1126 51.57800 0.305000 ... 3.397000 21.310000
[1608 rows x 6 columns]
Basic Statistical Information: X Train
Some basic statistic information of the designated data set:
SIO2(WT%) TIO2(WT%) ... FEOT(WT%) CAO(WT%)
count 1608.000000 1608.000000 ... 1608.000000 1608.000000
mean 52.113741 0.408489 ... 3.222394 21.465284
std 2.159623 0.426490 ... 1.505620 2.234392
min 0.218000 0.000000 ... 1.354000 0.097000
25% 51.349750 0.172750 ... 2.536859 20.570000
50% 52.170000 0.323500 ... 2.914616 21.600000
75% 52.974750 0.510000 ... 3.334250 22.417750
max 56.294137 6.970000 ... 17.950000 26.090000
[8 rows x 6 columns]
Successfully store 'X Train' in 'X Train.xlsx' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
-------------------------
The Selected Data Set: X Test
SIO2(WT%) TIO2(WT%) ... FEOT(WT%) CAO(WT%)
1317 53.651000 0.093000 ... 2.74500 23.32500
526 50.360000 1.000000 ... 2.81000 20.86000
393 53.878000 0.187000 ... 3.98200 20.24900
1405 52.714000 0.565000 ... 2.27200 21.17000
433 53.536487 0.371907 ... 2.96938 19.02568
... ... ... ... ... ...
733 51.780000 0.850000 ... 4.77000 21.32000
1474 54.935000 0.135000 ... 6.56400 1.00000
692 51.800000 0.800000 ... 2.95000 21.80000
1767 54.290000 0.390000 ... 2.52000 21.15000
1624 49.600000 0.580000 ... 3.20000 21.10000
[403 rows x 6 columns]
Basic Statistical Information: X Test
Some basic statistic information of the designated data set:
SIO2(WT%) TIO2(WT%) ... FEOT(WT%) CAO(WT%)
count 403.000000 403.000000 ... 403.000000 403.000000
mean 52.096260 0.422518 ... 3.189935 21.349220
std 1.916840 0.478066 ... 1.461479 2.657213
min 40.730000 0.000000 ... 1.281000 0.174000
25% 51.395000 0.147000 ... 2.531162 20.403500
50% 52.229000 0.300000 ... 2.938444 21.590000
75% 52.997000 0.535217 ... 3.333500 22.454500
max 56.301066 5.520000 ... 18.270000 25.171000
[8 rows x 6 columns]
Successfully store 'X Test' in 'X Test.xlsx' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
-------------------------
The Selected Data Set: Y Train
Label
261 1.0
607 1.0
1965 0.0
240 1.0
819 1.0
... ...
1130 1.0
1294 1.0
860 1.0
1459 1.0
1126 1.0
[1608 rows x 1 columns]
Basic Statistical Information: Y Train
Some basic statistic information of the designated data set:
Label
count 1608.000000
mean 0.730100
std 0.444046
min 0.000000
25% 0.000000
50% 1.000000
75% 1.000000
max 1.000000
Successfully store 'Y Train' in 'Y Train.xlsx' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
-------------------------
The Selected Data Set: Y Test
Label
1317 1.0
526 1.0
393 1.0
1405 1.0
433 1.0
... ...
733 1.0
1474 1.0
692 1.0
1767 0.0
1624 0.0
[403 rows x 1 columns]
Basic Statistical Information: Y Test
Some basic statistic information of the designated data set:
Label
count 403.000000
mean 0.751861
std 0.432470
min 0.000000
25% 1.000000
50% 1.000000
75% 1.000000
max 1.000000
Successfully store 'Y Test' in 'Y Test.xlsx' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
(Press Enter key to move forward.)
> Enter
4. Model Selection¶
Here, you should see a prompt to ask which model would you like to apply on the dataset you processed in the previous step:
-*-*- Model Selection -*-*-
1 - Logistic Regression
2 - Support Vector Machine
3 - Decision Tree
4 - Random Forest
5 - Extra-Trees
6 - XGBoost
7 - Multi-layer Perceptron
8 - Gradient Boosting
9 - K-Nearest Neighbors
10 - Stochastic Gradient Descent
11 - All models above to be trained
Which model do you want to apply?(Enter the Corresponding Number)
(Model) ➜ @Number: 6
> Enter
Here, we choose 6 - XGBoost as our model and let the program employing automated machine learning:
Do you want to employ automated machine learning with respect to
this algorithm?(Enter the Corresponding Number):
1 - Yes
2 - No
(Model) ➜ @Number: 1
> Enter
If needed, you can also cutomize your label via the step below, here, we skip this for the moment:
-*-*- Customize Label on Label Set -*-*-
1 - Yes
2 - No
(Data) ➜ @Number: 2
> Enter
That’s it!
Now, you should see XGBoost is running, just sit back and wait for the result:
*-**-* XGBoost is running ... *-**-*
Expected Functionality:
+ Model Score
+ Confusion Matrix
+ Cross Validation
+ Model Prediction
+ Model Persistence
+ Precision Recall Curve
+ ROC Curve
+ Two-dimensional Decision Boundary Diagram
+ Permutation Importance Diagram
+ Feature Importance Diagram
[flaml.automl: 02-06 15:02:04] {2599} INFO - task = classification
[flaml.automl: 02-06 15:02:04] {2601} INFO - Data split method: stratified
...
Be careful with the path storing your result, you can check it at the end of the output:
Successfully store 'Y Test Predict' in 'Y Test Predict.xlsx' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/data.
-----* Model Persistence *-----
Successfully store 'XGBoost' in 'XGBoost.pkl' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/model.
Successfully store 'XGBoost' in 'XGBoost.joblib' in
/Users/lcthw/geopi/geopi_output/GeoPi - Rock
Classification/XGBoost Algorithm - Test 1/artifacts/model.