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opt · optimization
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API reference
classes
AdamOptimizer
GradientDescent
MomentumOptimizer
RMSProp
functions
adam
backtrackingLineSearch
createOptimizer
curveFit
gradientDescent
lbfgs
leastSquares
makeBoundsTransform
methods
minimize
minimizeScalar
momentumDescent
nelderMead
numericalGradient
numericalHessian
numericalJacobian
rmsprop
rootScalar
solve
strongWolfeLineSearch
variables
default
proba · probability
Overview
API reference
@tangent.to
namespaces
special
functions
betainc
betaincInv
digamma
erf
erfc
gammainc
gammaincc
gammaincInv
lbeta
lchoose
lgamma
normalCdf
normalQuantile
special
functions
createRng
variables
bernoulli
beta
binomial
chi2
default
distributions
exponential
f
gamma
halfnormal
lognormal
normal
poisson
studentT
uniform
lina · linear algebra
Overview
API reference
functions
cholesky
choleskySolve
cond
det
diag
eigSym
identity
inv
isPositiveDefinite
lstsq
lu
matmul
norm
pinv
pinvSolve
qr
rank
solve
svd
trace
transpose
variables
default
ode · differential equations
Overview
API reference
functions
euler
rk2
rk4
rk45
rosenbrock
solve
variables
default
Applications
ds · data science
Overview
API reference
@tangent.to
namespaces
core
functions
applyFormula
parseFormula
namespaces
linalg
classes
Matrix
functions
covarianceMatrix
eig
inverse
mmul
pseudoInverse
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svd
toMatrix
transpose
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math
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approxEqual
guardFinite
guardPositive
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max
mean
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quantile
range
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variance
variables
E
EPSILON
PI
sd
std
math
optimize
functions
adam
backtrackingLineSearch
createOptimizer
curveFit
gradientDescent
lbfgs
leastSquares
makeBoundsTransform
methods
minimize
minimizeScalar
momentumDescent
nelderMead
numericalGradient
numericalHessian
numericalJacobian
rmsprop
rootScalar
solve
strongWolfeLineSearch
variables
AdamOptimizer
GradientDescent
MomentumOptimizer
RMSProp
optimize
persistence
functions
addSerializationSupport
deserializeValue
loadModel
makeSaveable
saveModel
serializeValue
persistence
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classes
KDTree
functions
buildKDTree
spatial
table
classes
LabelEncoder
OneHotEncoder
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applyColumns
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oneHotEncodeTable
prepareX
prepareXY
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toMatrix
toVector
table
core
ml
classes
BranchPipeline
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ConstantKernel
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GradientBoostingRegressor
GridSearchCV
HCA
IsolationForest
IterativeImputer
Kernel
KMeans
KNNClassifier
KNNImputer
KNNRegressor
LocalOutlierFactor
MahalanobisDistance
Matern
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Periodic
Pipeline
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Recipe
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SumKernel
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isolationForest
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knnImpute
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recipe
simpleImpute
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classificationError
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gini
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mae
mse
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dbscan
functions
fit
predict
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distances
functions
canberra
chebyshev
cosine
createGowerDistance
euclidean
getDistanceFunction
gower
hamming
manhattan
minkowski
distances
hca
functions
cut
cutHeight
fit
hca
interpret
functions
coefficientImportance
correlationMatrix
featureImportance
learningCurve
partialDependence
residualPlotData
interpret
kmeans
functions
fit
predict
silhouetteScore
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functions
crossEntropy
getLossFunction
hingeLoss
huberLoss
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metrics
functions
accuracy
adjustedRandIndex
cohenKappa
confusionMatrix
confusionMatrixText
f1
logLoss
mae
mse
precision
r2
recall
rmse
rocAuc
metrics
ml
explain
classes
KernelExplainer
PermutationExplainer
TreeExplainer
functions
importanceData
kernelShap
summaryData
treeShap
ml/explain
mlp
functions
createNetwork
evaluate
fit
predict
mlp
polynomial
functions
fit
polynomialFeatures
predict
polynomial
preprocessing
classes
LabelEncoder
MinMaxScaler
OneHotEncoder
PolynomialFeatures
StandardScaler
functions
cleanCategorical
fitPreprocessor
labelEncode
parseNumeric
preprocess
transformWithPipeline
variables
preprocessCategorical
preprocessing
silhouette
functions
silhouetteByCluster
silhouetteSamples
silhouette
train
functions
earlyStopping
learningRateScheduler
modelCheckpoint
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trainFunction
train
tuning
functions
GridSearchCV
RandomSearchCV
variables
distributions
tuning
utils
functions
random
randomInt
sample
setSeed
shuffle
utils
validation
functions
crossValidate
groupKFold
kFold
leaveOneOut
shuffleSplit
stratifiedKFold
trainTestSplit
validation
ml
mva
classes
CCA
LDA
PCA
RDA
namespaces
cca
functions
fit
transform
transformX
transformY
cca
composition
classes
CompositionalImputer
CompositionalOutlierDetector
functions
alr
alrInv
center
closure
clr
clrInv
compositionalOutliers
ilr
ilrInv
imputeMissing
inner
multiplicativeReplacement
power
sbpBasis
variables
centralize
composition
lda
functions
fit
predict
transform
lda
pca
functions
cumulativeVariance
fit
transform
pca
rda
functions
fit
transform
rda
mva
plot
functions
createD3DendrogramRenderer
dendrogramLayout
diagnosticDashboard
effectPlot
ordiplot
partialResidualPlot
plotCalibration
plotClusterMembership
plotClusterStability
plotCondensedTree
plotConfusionMatrix
plotCorrelationMatrix
plotFeatureImportance
plotHCA
plotHDBSCAN
plotHDBSCANDashboard
plotHDBSCANDendrogram
plotLearningCurve
plotPartialDependence
plotPrecisionRecall
plotQQ
plotResiduals
plotROC
plotScree
plotSilhouette
qqPlot
residualPlot
residualsLeveragePlot
resolveGroupValues
scaleLocationPlot
plot
stats
classes
ChiSquareTest
GLM
OneSampleTTest
OneWayAnova
TukeyHSD
TwoSampleTTest
functions
aicWeightPlot
bonferroni
coefficientComparisonPlot
cohensD
compareModels
crossValidate
crossValidateModels
etaSquared
fdr
fisherExactTest
holmBonferroni
leveneTest
likelihoodRatioTest
modelSelectionPlot
omegaSquared
pairwiseLRT
pearsonCorrelation
qchisq
spearmanCorrelation
variables
beta
chisq
chiSquareTest
gamma
hypothesis
kruskalWallis
mannWhitneyU
normal
oneSampleTTest
oneWayAnova
pairedTTest
tukeyHSD
twoSampleTTest
uniform
stats
variables
default
mc · Bayesian inference
Overview
API reference
classes
Bernoulli
Beta
Distribution
Gamma
HalfNormal
HamiltonianMC
HMC
Lognormal
MetropolisHastings
Model
Normal
NUTS
Uniform
functions
autocorrPlot
effectiveSampleSize
forestPlot
gelmanRubin
getRng
pairPlot
posteriorPlot
printSummary
rankPlot
setRandomSeed
summarize
summary
tracePlot
traceToCSV
traceToJSON
variables
default
diagnostics
distributions
plot
samplers
sem · structural equation modeling
Overview
API reference
functions
buildModel
parseModel
sampleCov
sem
variables
cfa
default
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special
Functions
betainc
betaincInv
digamma
erf
erfc
gammainc
gammaincc
gammaincInv
lbeta
lchoose
lgamma
normalCdf
normalQuantile