test passsed for docker file

Dockerfile

@@ -2,22 +2,23 @@ FROM continuumio/anaconda3
 
 # Install R
 RUN apt-get update
+RUN apt-get install libcurl4-openssl-dev -y
+RUN apt-get install libssl-dev -y
 RUN apt-get install r-base r-base-dev -y
 
 RUN conda install pandas
-RUN conda install warnings
 RUN conda install scikit-learn
 RUN conda install numpy
-RUN conda install datapackage
+RUN pip install datapackage
 RUN conda install requests
-
-RUN /opt/conda/bin/conda install -y -c anaconda docopt
+RUN conda install docopt
 
 # Install R Packages
 RUN Rscript -e "install.packages('tidyverse')"
 RUN Rscript -e "install.packages('knitr')"
 RUN Rscript -e "install.packages('docopt')"
 RUN Rscript -e "install.packages('ggplot2')"
+RUN Rscript -e "install.packages('reticulate')"
 
 # Put Anaconda Python in PATH
 ENV PATH="/opt/conda/bin:${PATH}"

doc/report.md

@@ -165,28 +165,28 @@ lgst_rgrsn = results.loc[15,'Random Forest']
 results
 ```
 
-    ##                     method         Random Forest
-    ## 0           Best_max_depth                   [3]
-    ## 1   Best_min_samples_split                   [3]
-    ## 2            Best_CV_Score                 [0.7]
-    ## 3           Training_Error  [0.2545454545454545]
-    ## 4         Validation_Error  [0.3571428571428571]
-    ## 5                   method         Decision Tree
-    ## 6           Best_max_depth                   [3]
-    ## 7    Bestmin_samples_split                   [2]
-    ## 8            Best_CV_Score   [0.640909090909091]
-    ## 9           Training_Error  [0.2772727272727272]
-    ## 10        Validation_Error  [0.3571428571428571]
-    ## 11                  method   Logistic regression
-    ## 12                       C                [0.01]
-    ## 13           Best_CV_Score  [0.7000000000000001]
-    ## 14          Training_Error  [0.2727272727272727]
-    ## 15        Validation_Error  [0.3214285714285714]
+    ##                     method          Random Forest
+    ## 0           Best_max_depth                    [3]
+    ## 1   Best_min_samples_split                    [3]
+    ## 2            Best_CV_Score   [0.7045454545454546]
+    ## 3           Training_Error  [0.25909090909090904]
+    ## 4         Validation_Error   [0.3035714285714286]
+    ## 5                   method          Decision Tree
+    ## 6           Best_max_depth                    [7]
+    ## 7    Bestmin_samples_split                    [3]
+    ## 8            Best_CV_Score   [0.6545454545454545]
+    ## 9           Training_Error   [0.1454545454545455]
+    ## 10        Validation_Error   [0.4107142857142857]
+    ## 11                  method    Logistic regression
+    ## 12                       C                    [1]
+    ## 13           Best_CV_Score   [0.7090909090909091]
+    ## 14          Training_Error   [0.2727272727272727]
+    ## 15        Validation_Error   [0.3214285714285714]
 
 In table 4 we show the results for all three models, including the best
 hyperparameters and the train and valdation errors. The depending on
 random influence, either the Random Forest model at
-\[0.3571428571428571\] or the Logistic Regression model at
+\[0.3035714285714286\] or the Logistic Regression model at
 \[0.3214285714285714\] had the best validation error. However, the
 Logistic Regression model was significantly less overfit than the Random
 Forest model since the difference between train and validation error was

results/analysis_result.csv

@@ -1,17 +1,17 @@
 method,Random Forest
 Best_max_depth,[3]
 Best_min_samples_split,[3]
-Best_CV_Score,[0.7]
-Training_Error,[0.2545454545454545]
-Validation_Error,[0.3571428571428571]
+Best_CV_Score,[0.7045454545454546]
+Training_Error,[0.25909090909090904]
+Validation_Error,[0.3035714285714286]
 method,Decision Tree
-Best_max_depth,[3]
-Bestmin_samples_split,[2]
-Best_CV_Score,[0.640909090909091]
-Training_Error,[0.2772727272727272]
-Validation_Error,[0.3571428571428571]
+Best_max_depth,[7]
+Bestmin_samples_split,[3]
+Best_CV_Score,[0.6545454545454545]
+Training_Error,[0.1454545454545455]
+Validation_Error,[0.4107142857142857]
 method,Logistic regression
-C,[0.01]
-Best_CV_Score,[0.7000000000000001]
+C,[1]
+Best_CV_Score,[0.7090909090909091]
 Training_Error,[0.2727272727272727]
 Validation_Error,[0.3214285714285714]