fix typos

Author: ablaom

src/fit_update.jl

@@ -96,7 +96,7 @@ Return an updated version of the `model` object returned by a previous [`fit`](@
 specify hyperparameter replacements in the form `p1=value1, p2=value2, ...`.
 
 When following the call `fit(algorithm, data)`, the `update` call is semantically
-equivalent to retraining ab initio using a concatentation of `data` and `new_data`,
+equivalent to retraining ab initio using a concatenation of `data` and `new_data`,
 *provided there are no hyperparameter replacements.* Behaviour is otherwise
 algorithm-specific.
 
@@ -131,7 +131,7 @@ Return an updated version of the `model` object returned by a previous [`fit`](@
 specify hyperparameter replacements in the form `p1=value1, p2=value2, ...`.
 
 When following the call `fit(algorithm, data)`, the `update` call is semantically
-equivalent to retraining ab initio using a concatentation of `data` and `new_data`,
+equivalent to retraining ab initio using a concatenation of `data` and `new_data`,
 *provided there are no hyperparameter replacements.* Behaviour is otherwise
 algorithm-specific.
 

src/target_weights_features.jl

@@ -5,7 +5,7 @@ Return, for each form of `data` supported in a call of the form [`fit(algorithm,
 data)`](@ref), the target variable part of `data`. If `nothing` is returned, the
 `algorithm` does not see a target variable in training (is unsupervised).
 
-Refer to LearnAPI.jl documenation for the precise meaning of "target".
+Refer to LearnAPI.jl documentation for the precise meaning of "target".
 
 # New implementations
 
@@ -22,7 +22,7 @@ target(::Any, data) = nothing
 
 Return, for each form of `data` supported in a call of the form `[`fit(algorithm,
 data)`](@ref), the per-observation weights part of `data`. Where `nothing` is returned, no
-weights are part of `data`, which is to be interpretted as uniform weighting.
+weights are part of `data`, which is to be interpreted as uniform weighting.
 
 # New implementations
 
@@ -51,7 +51,7 @@ ŷ = predict(algorithm, kind_of_proxy, X) # eg, `kind_of_proxy = Point()`
 
 The return value has the same number of observations as `data` does. For supervised models
 (i.e., where `:(LearnAPI.target) in LearnAPI.functions(algorithm)`) `ŷ` above is generally
-inteneded to be an approximate proxy for `LearnAPI.target(algorithm, data)`, the training
+intended to be an approximate proxy for `LearnAPI.target(algorithm, data)`, the training
 target.
 
 

src/traits.jl

@@ -87,7 +87,7 @@ function constructor end
 """
     LearnAPI.functions(algorithm)
 
-Return a tuple of expressions respresenting functions that can be meaningfully applied
+Return a tuple of expressions representing functions that can be meaningfully applied
 with `algorithm`, or an associated model (object returned by `fit(algorithm, ...)`, as the
 first argument. Algorithm traits (methods for which `algorithm` is the *only* argument)
 are excluded.
@@ -328,7 +328,7 @@ to return `"K-nearest neighbors regressor"`. Ideally, this is a "concrete" noun
 `"ridge regressor"` rather than an "abstract" noun like `"ridge regression"`.
 
 """
-human_name(algorithm) = snakecase(name(alogorithm), delim=' ') # `name` defined below
+human_name(algorithm) = snakecase(name(algorithm), delim=' ') # `name` defined below
 
 """
     LearnAPI.data_interface(algorithm)

typos.toml

@@ -0,0 +1,6 @@
+[default.extend-words]
+# Don't correct "mape" to "map"
+mape = "mape"
+yhat = "yhat"
+LSO ="LSO"
+datas = "datas"