fix formatting in roadmap

ROADMAP.md

@@ -10,43 +10,43 @@
 	  LearnAPI.jl
 	  
 - [ ] Flush out "Common Implementation Patterns". The current plan is to mock up example
-	  implementations, and add them as LearnAPI.jl tests, with links to the test file from
-	  "Common Implementation Patterns". As real-world implementations roll out, we could
-	  increasingly point to those instead, to conserve effort
-	  - [x] regression
-	  - [ ] classification
-      - [ ] clustering
-	  - [ ] gradient descent
-	  - [ ] iterative algorithms
-	  - [ ] incremental algorithms
-	  - [ ] dimension reduction
-	  - [x] feature engineering
-	  - [x] static algorithms
-	  - [ ] missing value imputation
-	  - [ ] transformers
-	  - [ ] ensemble algorithms
-	  - [ ] time series forecasting
-	  - [ ] time series classification
-	  - [ ] survival analysis
-	  - [ ] density estimation
-	  - [ ] Bayesian algorithms
-	  - [ ] outlier detection
-	  - [ ] collaborative filtering
-	  - [ ] text analysis
-	  - [ ] audio analysis
-	  - [ ] natural language processing
-	  - [ ] image processing
-	  - [ ] meta-algorithms
+  implementations, and add them as LearnAPI.jl tests, with links to the test file from
+  "Common Implementation Patterns". As real-world implementations roll out, we could
+  increasingly point to those instead, to conserve effort
+  - [x] regression
+  - [ ] classification
+  - [ ] clustering
+  - [ ] gradient descent
+  - [ ] iterative algorithms
+  - [ ] incremental algorithms
+  - [ ] dimension reduction
+  - [x] feature engineering
+  - [x] static algorithms
+  - [ ] missing value imputation
+  - [ ] transformers
+  - [ ] ensemble algorithms
+  - [ ] time series forecasting
+  - [ ] time series classification
+  - [ ] survival analysis
+  - [ ] density estimation
+  - [ ] Bayesian algorithms
+  - [ ] outlier detection
+  - [ ] collaborative filtering
+  - [ ] text analysis
+  - [ ] audio analysis
+  - [ ] natural language processing
+  - [ ] image processing
+  - [ ] meta-algorithms
 
 - [ ] In a utility package provide:
-    - [ ] Method to clone an algorithm with user-specified property(hyperparameter)
-          changes, as in `LearnAPI.clone(algorithm, p1=value1, p22=value2, ...)` (since
-          `algorithm` can have any type, can't really overload `Base.replace` without
-          piracy). This will be needed in tuning meta-algorithms. Or should this be in
-          LearnAPI.jl proper, to expose it to all users?
-	- [ ] Methods to facilitate common-use case data interfaces: support simultaneously
-          `fit` data of the form `data = (X, y)` where `X` is table *or* matrix, and
-          `data` a table with target specified by hyperparameter; here `obs` will return a
-          thin wrapping of the matrix of `X`, the target `y`, and the names of all
-          fields. We can have options to make `X` a concrete array or an adjoint,
-          depending on what is more efficient for the algorithm. 
+   - [ ] Method to clone an algorithm with user-specified property (hyperparameter)
+     replacement in `LearnAPI.clone(algorithm, p1=value1, p22=value2, ...)` (since
+     `algorithm` can have any type, can't really overload `Base.replace` without
+     piracy). This will be needed in tuning meta-algorithms. Or should this be in
+     LearnAPI.jl proper, to expose it to all users?
+   - [ ] Methods to facilitate common-use case data interfaces: support simultaneously
+     `fit` data of the form `data = (X, y)` where `X` is table *or* matrix, and `data` a
+     table with target specified by hyperparameter; here `obs` will return a thin wrapping
+     of the matrix of `X`, the target `y`, and the names of all fields. We can have
+     options to make `X` a concrete array or an adjoint, depending on what is more
+     efficient for the algorithm.