diff --git a/src/SVRfunctions.jl b/src/SVRfunctions.jl
index a86ada5..4c16ed5 100644
--- a/src/SVRfunctions.jl
+++ b/src/SVRfunctions.jl
@@ -114,7 +114,7 @@ function fit(y::AbstractArray{T}, x::AbstractArray{T}; kw...) where {T <: Number
 	return yp
 end
 
-function fit_test(y::AbstractVector{Float64}, x::AbstractArray{Float64}; ratio::Number=0.1, repeats::Number=1, pm=nothing, keepcases::Union{BitArray,Nothing}=nothing, scale::Bool=false, ymin::Number=minimum(y), ymax::Number=maximum(y), quiet::Bool=false, veryquiet::Bool=true, total::Bool=false, rmse::Bool=true, callback::Function=(y::AbstractVector, y_pr::AbstractVector, pm::AbstractVector)->nothing, kw...)
+function fit_test(y::AbstractVector{Float64}, x::AbstractArray{Float64}; ratio_prediction::Number=0.1, repeats::Number=1, mask_prediction=nothing, keepcases::Union{BitArray,Nothing}=nothing, scale::Bool=false, ymin::Number=minimum(y), ymax::Number=maximum(y), quiet::Bool=false, veryquiet::Bool=true, total::Bool=false, rmse::Bool=true, callback::Function=(y::AbstractVector, y_pr::AbstractVector, mask_prediction::AbstractVector)->nothing, kw...)
 	if !isnothing(keepcases)
 		@assert length(keepcases) == size(x, 2)
 	end
@@ -127,64 +127,64 @@ function fit_test(y::AbstractVector{Float64}, x::AbstractArray{Float64}; ratio::
 	pma = Vector{Bool}(undef, 0)
 	local y_pr
 	for r in 1:repeats
-		if repeats > 1 || isnothing(pm)
-			pm = get_prediction_mask(length(y), ratio; keepcases=keepcases)
+		if repeats > 1 || isnothing(mask_prediction)
+			mask_prediction = get_prediction_mask(length(y), ratio_prediction; keepcases=keepcases)
 		else
-			@assert length(pm) == size(x, 2)
-			@assert eltype(pm) <: Bool
+			@assert length(mask_prediction) == size(x, 2)
+			@assert eltype(mask_prediction) <: Bool
 		end
-		ic = sum(.!pm)
+		ic = sum(.!mask_prediction)
 		if !quiet && repeats == 1 && length(y) > ic
-			@info("Training on $(ic) out of $(length(y)) (prediction ratio $ratio) ...")
+			@info("Training on $(ic) out of $(length(y)) (prediction ratio_prediction $ratio_prediction) ...")
 		end
-		pmodel = train(a[.!pm], x[:,.!pm]; kw...)
+		pmodel = train(a[.!mask_prediction], x[:,.!mask_prediction]; kw...)
 		y_pr = predict(pmodel, x)
 		freemodel(pmodel)
 		if any(isnan.(y_pr))
 			@warn("SVR output contains NaN's!")
 		end
 		if rmse
-			m[r] = total ? rmse(y_pr, a) : rmse(y_pr[pm], a[pm])
+			m[r] = total ? rmse(y_pr, a) : rmse(y_pr[mask_prediction], a[mask_prediction])
 		else
-			m[r] = total ? r2(y_pr, a) : r2(y_pr[pm], a[pm])
+			m[r] = total ? r2(y_pr, a) : r2(y_pr[mask_prediction], a[mask_prediction])
 		end
 		if !veryquiet && repeats > 1
 			println("Repeat $r: $(m[r])")
 		end
 		y_pra = vcat(y_pra, y_pr)
 		ya = vcat(ya, y)
-		pma = vcat(pma, pm)
+		pma = vcat(pma, mask_prediction)
 	end
 	y_pra = y_pra * (ymax - ymin) .+ ymin
 	callback(ya, y_pra, pma)
 	y_pr = y_pr * (ymax - ymin) .+ ymin
-	return y_pr, pm, Statistics.mean(m)
+	return y_pr, mask_prediction, Statistics.mean(m)
 end
-function fit_test(y::AbstractVector{T}, x::AbstractArray{T}; ratio::Number=0.1, kw...) where {T <: Number}
-	y_pr, pm, rmse = fit_test(Float64.(y), Float64.(x); ratio=ratio, kw...)
-	return T.(y_pr), pm, rmse
+function fit_test(y::AbstractVector{T}, x::AbstractArray{T}; ratio_prediction::Number=0.1, kw...) where {T <: Number}
+	y_pr, mask_prediction, rmse = fit_test(Float64.(y), Float64.(x); ratio_prediction=ratio_prediction, kw...)
+	return T.(y_pr), mask_prediction, rmse
 end
-function fit_test(y::AbstractArray{T}, x::AbstractArray{T}; ratio::Number=0.1, pm=nothing, keepcases::Union{BitArray,Nothing}=nothing, kw...) where {T <: Number}
+function fit_test(y::AbstractArray{T}, x::AbstractArray{T}; ratio_prediction::Number=0.1, mask_prediction=nothing, keepcases::Union{BitArray,Nothing}=nothing, kw...) where {T <: Number}
 	@assert size(y, 1) == size(x, 2)
 	if !isnothing(keepcases)
 		@assert length(keepcases) == size(x, 2)
 	end
-	if isnothing(pm)
-		pm = get_prediction_mask(size(y, 1), ratio; keepcases=keepcases)
+	if isnothing(mask_prediction)
+		mask_prediction = get_prediction_mask(size(y, 1), ratio_prediction; keepcases=keepcases)
 	end
 	yp = similar(y)
 	for i = 1:size(y, 2)
-		yp[:,i], _, rmse = fit_test(vec(y[:,i]), x; ratio=ratio, pm=pm, kw...)
+		yp[:,i], _, rmse = fit_test(vec(y[:,i]), x; ratio_prediction=ratio_prediction, mask_prediction=mask_prediction, kw...)
 	end
-	return yp, pm, rmse
+	return yp, mask_prediction, rmse
 end
-function fit_test(y::AbstractVector{T}, x::AbstractArray{T}, vattr::Union{AbstractVector,AbstractRange}; ratio::Number=0.1, attr=:gamma, rmse::Bool=true, check::Function=(v::AbstractVector)->nothing, kw...) where {T <: Number}
+function fit_test(y::AbstractVector{T}, x::AbstractArray{T}, vattr::Union{AbstractVector,AbstractRange}; ratio_prediction::Number=0.1, attr=:gamma, rmse::Bool=true, check::Function=(v::AbstractVector)->nothing, kw...) where {T <: Number}
 	@assert length(vattr) > 0
-	@info("Grid search on $attr with prediction ratio $ratio ...")
+	@info("Grid search on $attr with prediction ratio_prediction $ratio_prediction ...")
 	ma = Vector{T}(undef, length(vattr))
 	for (i, g) in enumerate(vattr)
 		k = Dict(attr=>g)
-		y_pr, pm, ma[i] = fit_test(y, x; ratio=ratio, rmse=rmse, kw..., k..., quiet=true)
+		y_pr, mask_prediction, ma[i] = fit_test(y, x; ratio_prediction=ratio_prediction, rmse=rmse, kw..., k..., quiet=true)
 		@info("$attr=>$g: $(ma[i])")
 	end
 	c = check(ma)
@@ -195,42 +195,42 @@ function fit_test(y::AbstractVector{T}, x::AbstractArray{T}, vattr::Union{Abstra
 		m = ma[i]
 	end
 	k = Dict(attr=>vattr[i])
- 	return m, vattr[i], fit_test(y, x; ratio=ratio, rmse=rmse, kw..., k..., repeats=1)...
+ 	return m, vattr[i], fit_test(y, x; ratio_prediction=ratio_prediction, rmse=rmse, kw..., k..., repeats=1)...
 end
-function fit_test(y::AbstractVector{T}, x::AbstractArray{T}, vattr1::Union{AbstractVector,AbstractRange}, vattr2::Union{AbstractVector,AbstractRange}; ratio::Number=0.1, attr1=:gamma, attr2=:epsilon, rmse::Bool=true, kw...) where {T <: Number}
+function fit_test(y::AbstractVector{T}, x::AbstractArray{T}, vattr1::Union{AbstractVector,AbstractRange}, vattr2::Union{AbstractVector,AbstractRange}; ratio_prediction::Number=0.1, attr1=:gamma, attr2=:epsilon, rmse::Bool=true, kw...) where {T <: Number}
 	@assert length(vattr1) > 0
 	@assert length(vattr2) > 0
-	@info("Grid search on $attr1/$attr2 with prediction ratio $ratio ...")
+	@info("Grid search on $attr1/$attr2 with prediction ratio_prediction $ratio_prediction ...")
 	ma = Matrix{T}(undef, length(vattr1), length(vattr2))
 	for (i, a1) in enumerate(vattr1)
 		for (j, a2) in enumerate(vattr2)
 			k = Dict(attr1=>a1, attr2=>a2)
-			y_pr, pm, ma[i, j] = fit_test(y, x; ratio=ratio, rmse=rmse, kw..., k...)
+			y_pr, mask_prediction, ma[i, j] = fit_test(y, x; ratio_prediction=ratio_prediction, rmse=rmse, kw..., k...)
 			@info("$attr1=>$a1 $attr2=>$a2: $(ma[i,j])")
 		end
 	end
 	m, i = rmse ? findmin(ma) : findmax(ma)
 	k = Dict(attr1=>vattr1[i.I[1]], attr2=>vattr2[i.I[2]])
-	return m, vattr1[i.I[1]], vattr2[i.I[2]], fit_test(y, x; ratio=ratio, rmse=rmse, kw..., k..., repeats=1)...
+	return m, vattr1[i.I[1]], vattr2[i.I[2]], fit_test(y, x; ratio_prediction=ratio_prediction, rmse=rmse, kw..., k..., repeats=1)...
 end
-function fit_test(y::AbstractVector{T}, x::AbstractArray{T}, vattr1::Union{AbstractVector,AbstractRange}, vattr2::Union{AbstractVector,AbstractRange}, vattr3::Union{AbstractVector,AbstractRange}; ratio::Number=0.1, attr1=:gamma, attr2=:epsilon, attr3=:C, rmse::Bool=true, kw...) where {T <: Number}
+function fit_test(y::AbstractVector{T}, x::AbstractArray{T}, vattr1::Union{AbstractVector,AbstractRange}, vattr2::Union{AbstractVector,AbstractRange}, vattr3::Union{AbstractVector,AbstractRange}; ratio_prediction::Number=0.1, attr1=:gamma, attr2=:epsilon, attr3=:C, rmse::Bool=true, kw...) where {T <: Number}
 	@assert length(vattr1) > 0
 	@assert length(vattr2) > 0
 	@assert length(vattr3) > 0
-	@info("Grid search on $attr1/$attr2/$attr3 with prediction ratio $ratio ...")
+	@info("Grid search on $attr1/$attr2/$attr3 with prediction ratio_prediction $ratio_prediction ...")
 	ma = Array{T}(undef, length(vattr1), length(vattr2), length(vattr3))
 	for (i, a1) in enumerate(vattr1)
 		for (j, a2) in enumerate(vattr2)
 			for (k, a3) in enumerate(vattr3)
 				kk = Dict(attr1=>a1, attr2=>a2, attr3=>a3)
-				y_pr, pm, ma[i, j, k] = fit_test(y, x; ratio=ratio, rmse=rmse, kw..., kk...)
+				y_pr, mask_prediction, ma[i, j, k] = fit_test(y, x; ratio_prediction=ratio_prediction, rmse=rmse, kw..., kk...)
 				@info("$attr1=>$a1 $attr2=>$a2 $attr3=>$a3: $(ma[i,j,k])")
 			end
 		end
 	end
 	m, i = rmse ? findmin(ma) : findmax(ma)
 	k = Dict(attr1=>vattr1[i.I[1]], attr2=>vattr2[i.I[2]], attr3=>vattr3[i.I[3]])
-	return m, vattr1[i.I[1]], vattr2[i.I[2]], vattr3[i.I[3]], fit_test(y, x; ratio=ratio, rmse=rmse, kw..., k..., repeats=1)...
+	return m, vattr1[i.I[1]], vattr2[i.I[2]], vattr3[i.I[3]], fit_test(y, x; ratio_prediction=ratio_prediction, rmse=rmse, kw..., k..., repeats=1)...
 end
 
 """
@@ -238,21 +238,21 @@ Get prediction mask
 
 $(DocumentFunction.documentfunction(get_prediction_mask;
 argtext=Dict("ns"=>"number of samples",
-            "ratio"=>"prediction ratio")))
+            "ratio_prediction"=>"prediction ratio_prediction")))
 
 Return:
 
 - prediction mask
 """
-function get_prediction_mask(ns::Number, ratio::Number; keepcases::Union{AbstractVector,Nothing}=nothing, debug::Bool=false)
+function get_prediction_mask(ns::Number, ratio_prediction::Number; keepcases::Union{AbstractVector,Nothing}=nothing, debug::Bool=false)
 	nsi = copy(ns)
-	pm = trues(ns)
-	ic = convert(Int64, ceil(ns * (1. - ratio)))
+	mask_prediction = trues(ns)
+	ic = convert(Int64, ceil(ns * (1. - ratio_prediction)))
 	if !isnothing(keepcases)
-		@assert length(keepcases) == length(pm)
+		@assert length(keepcases) == length(mask_prediction)
 		kn = sum(keepcases)
 		if ic > kn && ns > kn
-			pm[keepcases] .= false
+			mask_prediction[keepcases] .= false
 			ic -= kn
 			nsi -= kn
 		else
@@ -264,16 +264,16 @@ function get_prediction_mask(ns::Number, ratio::Number; keepcases::Union{Abstrac
 		if !isnothing(keepcases) && ic > kn
 			m = trues(nsi)
 			m[ir] .= false
-			pm[.!keepcases] .= m
+			mask_prediction[.!keepcases] .= m
 		else
-			pm[ir] .= false
+			mask_prediction[ir] .= false
 		end
 	end
 	if debug
-		@info("Number of cases for training: $(ns - sum(pm))")
-		@info("Number of cases for prediction: $(sum(pm))")
+		@info("Number of cases for training: $(ns - sum(mask_prediction))")
+		@info("Number of cases for prediction: $(sum(mask_prediction))")
 	end
-	return pm
+	return mask_prediction
 end
 
 """