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Predict multiple value based on another value
legeithien
Member Posts: 3 
Learner I
in Help
Hello guys,
so, i had this data which represents the contents of soil in x-depth and the data only has limited depth. i tried to make model based on contents of soil in x-depth which going to predict those contents of soil arent shown on the depth. e.g contents of soil in 3 m depths and make this data to predict contents of soil in 5m depth
i tried using impute missing values operator to estimate those missing values of contents and using naive bayes, k-NN. i was going to use at least 3 predictions but every time i tried it kept getting errors, so im only using those two for now.
my question is, is it correct? or is there any methods that i could tried to achieve better results?
Thanks in advance!
so, i had this data which represents the contents of soil in x-depth and the data only has limited depth. i tried to make model based on contents of soil in x-depth which going to predict those contents of soil arent shown on the depth. e.g contents of soil in 3 m depths and make this data to predict contents of soil in 5m depth
i tried using impute missing values operator to estimate those missing values of contents and using naive bayes, k-NN. i was going to use at least 3 predictions but every time i tried it kept getting errors, so im only using those two for now.
<?xml version="1.0" encoding="UTF-8"?><process version="9.9.000">
<context>
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<operator activated="true" class="read_excel" compatibility="9.9.000" expanded="true" height="68" name="Read Excel" width="90" x="45" y="34">
<parameter key="excel_file" value="C:\Users\Avengers\Documents\RapidMiner\Local Repository\fyp\another part1.xlsx"/>
<parameter key="sheet_selection" value="sheet number"/>
<parameter key="sheet_number" value="1"/>
<parameter key="imported_cell_range" value="A1"/>
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<parameter key="time_zone" value="SYSTEM"/>
<parameter key="locale" value="English (United States)"/>
<parameter key="read_all_values_as_polynominal" value="false"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="Depth (m).true.real.attribute"/>
<parameter key="1" value="Gravel.true.integer.attribute"/>
<parameter key="2" value="Sand.true.real.attribute"/>
<parameter key="3" value="Silt.true.real.attribute"/>
<parameter key="4" value="Clay.true.integer.attribute"/>
<parameter key="5" value="LL.true.integer.attribute"/>
<parameter key="6" value="PL.true.integer.attribute"/>
<parameter key="7" value="PI.true.integer.attribute"/>
<parameter key="8" value="Moisture Content (%).true.real.attribute"/>
<parameter key="9" value="Bulk Density (Mg/m3).true.real.attribute"/>
<parameter key="10" value="Dry Density (Mg/m3).true.real.attribute"/>
<parameter key="11" value="Specific Gravity.true.real.attribute"/>
<parameter key="12" value="SPT (N).true.integer.attribute"/>
<parameter key="13" value="Type of Soil.true.polynominal.attribute"/>
<parameter key="14" value="Soil Condition.true.polynominal.attribute"/>
<parameter key="15" value="Resistivity (Ohm\.m).true.real.attribute"/>
<parameter key="16" value="Seismic, Vp (km/s).true.real.attribute"/>
</list>
<parameter key="read_not_matching_values_as_missings" value="true"/>
</operator>
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<parameter key="value_type" value="attribute_value"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="time"/>
<parameter key="block_type" value="attribute_block"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="value_matrix_row_start"/>
<parameter key="invert_selection" value="false"/>
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<parameter key="attribute_name" value="Type of Soil"/>
<parameter key="target_role" value="prediction"/>
<list key="set_additional_roles">
<parameter key="Type of Soil" value="label"/>
</list>
</operator>
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<parameter key="order" value="chronological"/>
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<parameter key="correlation" value="false"/>
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</operator>
</process>my question is, is it correct? or is there any methods that i could tried to achieve better results?
Thanks in advance!
0
Answers
Dortmund, Germany
will try to do that and give the feedback, thanks for responding it
edit: after i look further into it, i dont think it was a suitable method for my data because my data has a time series of "depth" but i need to look for the content of soil on that depth which the depth doesnt have to be a series. what i was looking for is how do i predict the content of that depth