Usage.md

 ## Package Installation
-Install any packages you want to use. For our example, switch to the package manager with `\]` and run:
+
+Install any packages you want to use. For our example, switch to the package manager with `]` and run:
+
 ```julia
 add LowRankModels
 add DecisionTree
 ```
 
 ## Quickstart
+
 Congratulations! As an example, run:
 
 ```julia
@@ -23,6 +26,7 @@ Z = Vector{String}()
 data = Vector{Vector{Float64}}()
 
 # separate data from metadata columns
+# change this as appropriate for your data or generate some dummy data
 for rline in readlines(toy)
       line = split(rline,',')
       push!(T, parse(Int,line[353]))
@@ -36,21 +40,42 @@ block = ConML.VMS{Float64}(T, Sigma, Z, data)
 
 # set parameters
 # we have to specify values for which no defaults exist
-par = ConML.ParametersConML(LearnBlockMinimum = 1, maxCategories = 10, MinCategorySize = 20, maxFilterFeatures = 1000, maxFilterSamples = 2000)
+par = ConML.ParametersConML(LearnBlockMinimum = 1, MinCategorySize = 20, maxFilterFeatures = 1000, maxFilterSamples = 2000)
 # create empty knowledge base
-kb = ConML.KnowledgeBase{Int}(ConML.VMS{Int}(),Vector{ConML.MachineModel}())
+kb = ConML.KnowledgeBase()
 # create steps for the algorithm
-myConstruction = ConML.Construct([LowRankModels.KMeans(k=2), LowRankModels.KMeans(k=3), LowRankModels.KMeans(k=4)])
+myConstruction = ConML.Construct([LowRankModels.KMeans(k=k) for k in 2:4])
 myReconstruction = ConML.Reconstruct([DecisionTree.DecisionTreeClassifier(max_depth=2), DecisionTree.AdaBoostStumpClassifier(n_iterations=10)])
-featureSelection = ConML.FeatureSelector()
+# Skip feature selection for now, see ConMLDefaults for default feature selectors
+featureSelection = nothing
 
 # make a pipeline
-learn = ConML.LearnerConML(kb, par, [ConML.searchLearnBlocks, myConstruction, featureSelection, myReconstruction])
+learn = ConML.LearnerConML(kb, par, myConstruction, featureSelection, myReconstruction; skipLearnblockSearch = false)
 
 # feed it!
 learn(block)
-
 ```
 
 ## Update
-Navigate to the project folder and run `git pull`. Afterwards, go to the julia package manager (`\]`) and run `update`.
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+
+Navigate to the project folder and run `git pull`. Afterwards, go to the julia package manager (`]`) and run `update`.
+
+## Help
+
+To see how something works, enter help mode with `?` and type the name of the object you want to know about, e.g.:
+
+```julia
+help?> Reconstruct
+
+  Reconstruct(estimators, names; winnerselection = select_best_or_none)
+  Reconstruct(pairs(estimator, name))
+  Reconstruct(pairs(name, estimator))
+  Reconstruct(dictionary(estimator,name))
+  Reconstruct(dictionary(name, estimator))
+
+  Create a callable object for Reconstruction. Estimators must be subtypes of scikit-learn BaseEstimator and implement the scikit-learn API for supervised methods (namely, fit!(method, Xs, Ys) and predict(method, Xs) are expected).
+
+...
+```
+
+If something is still lacking in documentation, take a look at the source code or send us an email.
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