David Boyd dthboyd

## Tensorflow for Poets.ipynb

      
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                dthboyd
                / Tensorflow for Poets.ipynb
            
            
              Created
              March 25, 2018 16:29
                — forked from bourdakos1/Tensorflow for Poets.ipynb
            
          
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## roc.ipynb

      
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                dthboyd
                / roc.ipynb
            
            
              Created
              April 17, 2018 00:09
                — forked from anonymous/roc.ipynb
            
              
                ROC random forest
              
          
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## dask-xgboost-airlines.ipynb

      
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                dthboyd
                / dask-xgboost-airlines.ipynb
            
            
              Created
              June 11, 2018 18:29
                — forked from mrocklin/dask-xgboost-airlines.ipynb
            
          
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## caseWhen2.ipynb

      
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                dthboyd
                / caseWhen2.ipynb
            
            
              Created
              August 4, 2018 01:31
                — forked from heyrudder/caseWhen2.ipynb
            
              
                How to do an IF / CASE statement in Pandas
              
          
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## XGBoost_classifier.py
model = XGBClassifier(silent=False,
                      scale_pos_weight=1,
                      learning_rate=0.01,
                      colsample_bytree = 0.4,
                      subsample = 0.8,
                      objective='binary:logistic',
                      n_estimators=1000,
                      reg_alpha = 0.3,
                      max_depth=4,
                      gamma=10)

## ML_for_Balancing.ipynb

      
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                dthboyd
                / ML_for_Balancing.ipynb
            
            
              Created
              August 11, 2018 17:41
            
              
                Machine Learning for Game Balancing
              
          
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## exploratory_data_analysis.py
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import missingno
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
	model = XGBClassifier(silent=False,
	scale_pos_weight=1,
	learning_rate=0.01,
	colsample_bytree = 0.4,
	subsample = 0.8,
	objective='binary:logistic',
	n_estimators=1000,
	reg_alpha = 0.3,
	max_depth=4,
	gamma=10)
	import pandas as pd
	import numpy as np
	import matplotlib
	import matplotlib.pyplot as plt
	import seaborn as sns
	import missingno
	import warnings
	warnings.filterwarnings("ignore")
	%matplotlib inline