A four-state learning curve: training and validation score against training-set size, each inside a ±1 SD band computed from the per-fold scores, with the gap at the largest size bracketed and read as high variance, still converging, still learning, high bias or converged.
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status="ready" — 8 training sizes, 5 folds each. Training holds at 0.966 while validation plateaus at 0.797, and across the tail the 0.169 gap moved 0.015 inside ±0.032 of noise. Read: high variance.
Gradient boosting · learning curve
High variance
8 sizes · 400 to 3,200 samples · 5 folds · gap 0.169 at the largest size
High variance — the gap is not closinggap 0.169 at 3,200 samples
Training sits 0.169 above validation at the largest size, and across the upper half of the sweep the gap moved by 0.015 — inside its own ±0.032 noise, so it has not measurably closed. The model is fitting the training split; regularisation, fewer features or a smaller hypothesis space are the levers. Validation moved 0.011 across the same stretch against its ±0.028 noise, so more rows alone are not currently buying much.
Each line is the mean score across the folds at that training size and each band is ±1 sample standard deviation of those same fold scores, so a wide band means the folds disagreed, not that the score moved. The gap is measured fold by fold — training minus validation within one split — and the read only calls something a trend when it clears twice the standard error of that difference, measured over every size from 1,600 samples onward. A negative gap, validation above training, is what heavy regularisation looks like and is printed as it comes. Point at the plot, or tab in and use the arrow keys, to read any size.
Learning curve: R² against training-set size, 8 sizes from 400 to 3,200 training samples, 5 folds per size. Each band is the mean ± one sample standard deviation across the folds at that size. At the largest size training scores 0.966 and validation 0.797, a gap of 0.169. Read: high variance — the gap is not closing. The release target is 0.860. Exact values follow in the data table. Up to 16 sizes, evenly spaced by rank.
Training samples
Train R²
Validation R²
Gap
Folds
400
0.971 ± 0.018
0.691 ± 0.060
0.280 ± 0.071
5
800
0.974 ± 0.017
0.760 ± 0.025
0.214 ± 0.029
5
1,200
0.967 ± 0.006
0.764 ± 0.034
0.203 ± 0.031
5
1,600
0.970 ± 0.009
0.786 ± 0.027
0.185 ± 0.030
5
2,000
0.966 ± 0.005
0.808 ± 0.021
0.158 ± 0.023
5
2,400
0.966 ± 0.004
0.784 ± 0.010
0.182 ± 0.008
5
2,800
0.966 ± 0.003
0.802 ± 0.015
0.164 ± 0.014
5
3,200
0.966 ± 0.006
0.797 ± 0.016
0.169 ± 0.019
5
the same over-fitting shape caught earlier — the 0.065 gap is still real against ±0.009 of fold noise, but it narrowed 0.037 across the tail. Read: still converging, the verdict that keeps a live gap from ever being reported as met.
Random forest · learning curve
Still converging
8 sizes · 400 to 3,200 samples · 5 folds · gap 0.065 at the largest size
Still converging — the gap is real but closinggap 0.065 at 3,200 samples
Training still sits 0.065 above validation at the largest size — clear of the ±0.009 fold noise on that gap — but across the upper half of the sweep it narrowed by 0.037, past its own ±0.013. The curves have not met yet and are still moving toward each other, with validation itself up 0.039 across the same stretch, so the sweep is what to extend: take the bias/variance read again once the gap stops shrinking.
Each line is the mean score across the folds at that training size and each band is ±1 sample standard deviation of those same fold scores, so a wide band means the folds disagreed, not that the score moved. The gap is measured fold by fold — training minus validation within one split — and the read only calls something a trend when it clears twice the standard error of that difference, measured over every size from 1,600 samples onward. A negative gap, validation above training, is what heavy regularisation looks like and is printed as it comes. Point at the plot, or tab in and use the arrow keys, to read any size.
Learning curve: R² against training-set size, 8 sizes from 400 to 3,200 training samples, 5 folds per size. Each band is the mean ± one sample standard deviation across the folds at that size. At the largest size training scores 0.954 and validation 0.890, a gap of 0.065. Read: still converging — the gap is real but closing. The release target is 0.860. Exact values follow in the data table. Up to 16 sizes, evenly spaced by rank.
Training samples
Train R²
Validation R²
Gap
Folds
400
0.962 ± 0.009
0.698 ± 0.041
0.264 ± 0.048
5
800
0.949 ± 0.007
0.785 ± 0.021
0.164 ± 0.022
5
1,200
0.958 ± 0.010
0.807 ± 0.024
0.151 ± 0.018
5
1,600
0.953 ± 0.007
0.851 ± 0.013
0.101 ± 0.011
5
2,000
0.950 ± 0.008
0.867 ± 0.016
0.083 ± 0.020
5
2,400
0.951 ± 0.008
0.884 ± 0.012
0.067 ± 0.018
5
2,800
0.953 ± 0.006
0.878 ± 0.010
0.075 ± 0.010
5
3,200
0.954 ± 0.005
0.890 ± 0.009
0.065 ± 0.010
5
a well-regularised model the sweep stopped too early on — the curves sit on top of each other (0.003 apart, inside the fold noise) and validation is still up 0.065 across the tail. Read: still learning, so no bias/variance call is available yet.
Linear SVM · learning curve
Still learning
8 sizes · 400 to 3,200 samples · 5 folds · gap 0.0033 at the largest size
Still learning — validation is still climbinggap 0.0033 at 3,200 samples
Validation gained 0.0648 across the upper half of the sweep, clearing its ±0.0177 noise, so the curve has not flattened yet. The sweep stopped before the model did — collect or label more data before drawing a bias/variance conclusion.
Each line is the mean score across the folds at that training size and each band is ±1 sample standard deviation of those same fold scores, so a wide band means the folds disagreed, not that the score moved. The gap is measured fold by fold — training minus validation within one split — and the read only calls something a trend when it clears twice the standard error of that difference, measured over every size from 1,600 samples onward. A negative gap, validation above training, is what heavy regularisation looks like and is printed as it comes. Point at the plot, or tab in and use the arrow keys, to read any size.
Learning curve: R² against training-set size, 8 sizes from 400 to 3,200 training samples, 5 folds per size. Each band is the mean ± one sample standard deviation across the folds at that size. At the largest size training scores 0.7981 and validation 0.7948, a gap of 0.0033. Read: still learning — validation is still climbing. The release target is 0.8600. Exact values follow in the data table. Up to 16 sizes, evenly spaced by rank.
Training samples
Train R²
Validation R²
Gap
Folds
400
0.6429 ± 0.0315
0.6528 ± 0.0273
-0.0098 ± 0.0437
5
800
0.6984 ± 0.0311
0.6869 ± 0.0180
0.0115 ± 0.0356
5
1,200
0.7052 ± 0.0247
0.7142 ± 0.0147
-0.0090 ± 0.0314
5
1,600
0.7374 ± 0.0177
0.7300 ± 0.0164
0.0074 ± 0.0262
5
2,000
0.7583 ± 0.0188
0.7513 ± 0.0183
0.0070 ± 0.0227
5
2,400
0.7682 ± 0.0174
0.7697 ± 0.0228
-0.0015 ± 0.0288
5
2,800
0.7896 ± 0.0129
0.7776 ± 0.0148
0.0120 ± 0.0115
5
3,200
0.7981 ± 0.0100
0.7948 ± 0.0111
0.0033 ± 0.0178
5
the finished sweep — the curves met at 0.890, nothing is moving and the release target is behind them. Read: converged, and extra rows buy nothing here.
Ridge regression · learning curve
Converged
8 sizes · 400 to 3,200 samples · 5 folds · gap 0.0031 at the largest size
Converged — the curves have met and flattenedgap 0.0031 at 3,200 samples
The gap of 0.0031 is inside the fold noise and validation has stopped climbing, so the curves have met and flattened at or above the release target. Extra rows buy little here; a better score needs a different model or better features.
Each line is the mean score across the folds at that training size and each band is ±1 sample standard deviation of those same fold scores, so a wide band means the folds disagreed, not that the score moved. The gap is measured fold by fold — training minus validation within one split — and the read only calls something a trend when it clears twice the standard error of that difference, measured over every size from 1,600 samples onward. A negative gap, validation above training, is what heavy regularisation looks like and is printed as it comes. Point at the plot, or tab in and use the arrow keys, to read any size.
Learning curve: R² against training-set size, 8 sizes from 400 to 3,200 training samples, 5 folds per size. Each band is the mean ± one sample standard deviation across the folds at that size. At the largest size training scores 0.8933 and validation 0.8902, a gap of 0.0031. Read: converged — the curves have met and flattened. The release target is 0.8600. Exact values follow in the data table. Up to 16 sizes, evenly spaced by rank.
Training samples
Train R²
Validation R²
Gap
Folds
400
0.8389 ± 0.0118
0.8565 ± 0.0111
-0.0176 ± 0.0210
5
800
0.8910 ± 0.0160
0.8741 ± 0.0198
0.0169 ± 0.0124
5
1,200
0.8867 ± 0.0116
0.8850 ± 0.0161
0.0017 ± 0.0190
5
1,600
0.8954 ± 0.0120
0.8910 ± 0.0133
0.0045 ± 0.0188
5
2,000
0.8986 ± 0.0106
0.8834 ± 0.0048
0.0151 ± 0.0101
5
2,400
0.8970 ± 0.0046
0.8876 ± 0.0084
0.0094 ± 0.0076
5
2,800
0.8993 ± 0.0082
0.8936 ± 0.0061
0.0056 ± 0.0085
5
3,200
0.8933 ± 0.0063
0.8902 ± 0.0080
0.0031 ± 0.0117
5
met, flat and nowhere near good enough — validation settles at 0.736 against a 0.860 target. Read: high bias. The target is so far above the data that it is left off the y axis and marked with a caret at the edge it left by, rather than squashing both curves into a ribbon.
Logistic regression · learning curve
High bias
8 sizes · 400 to 3,200 samples · 5 folds · gap 0.0037 at the largest size
High bias — converged below the targetgap 0.0037 at 3,200 samples
The two curves have met — the gap of 0.0037 is inside the fold noise — and validation has stopped climbing, but it settled at 0.7358, below the release target of 0.8600. More data will not close a gap to the target; the model, the features or the target has to change.
Each line is the mean score across the folds at that training size and each band is ±1 sample standard deviation of those same fold scores, so a wide band means the folds disagreed, not that the score moved. The gap is measured fold by fold — training minus validation within one split — and the read only calls something a trend when it clears twice the standard error of that difference, measured over every size from 1,600 samples onward. A negative gap, validation above training, is what heavy regularisation looks like and is printed as it comes. Point at the plot, or tab in and use the arrow keys, to read any size.
Learning curve: R² against training-set size, 8 sizes from 400 to 3,200 training samples, 5 folds per size. Each band is the mean ± one sample standard deviation across the folds at that size. At the largest size training scores 0.7395 and validation 0.7358, a gap of 0.0037. Read: high bias — converged below the target. The release target is 0.8600, outside the plotted range. Exact values follow in the data table. Up to 16 sizes, evenly spaced by rank.
Training samples
Train R²
Validation R²
Gap
Folds
400
0.7118 ± 0.0263
0.7066 ± 0.0129
0.0051 ± 0.0211
5
800
0.7440 ± 0.0071
0.7292 ± 0.0159
0.0148 ± 0.0180
5
1,200
0.7547 ± 0.0076
0.7423 ± 0.0114
0.0124 ± 0.0052
5
1,600
0.7406 ± 0.0104
0.7351 ± 0.0084
0.0055 ± 0.0176
5
2,000
0.7508 ± 0.0094
0.7454 ± 0.0138
0.0054 ± 0.0177
5
2,400
0.7426 ± 0.0110
0.7367 ± 0.0112
0.0059 ± 0.0136
5
2,800
0.7452 ± 0.0100
0.7459 ± 0.0072
-0.0006 ± 0.0142
5
3,200
0.7395 ± 0.0076
0.7358 ± 0.0096
0.0037 ± 0.0123
5
status="loading" — a flat top line, a rising lower one and a narrowing band, so the skeleton is the shape of the thing being loaded
Gradient boosting · learning curve
Training and validation R² against training-set size, each with a ±1 SD band across folds
Loading the learning curve
status="empty" — no sweep yet, worded so it cannot be read as a failed fetch
Gradient boosting · learning curve
Training and validation R² against training-set size, each with a ±1 SD band across folds
No sweep to plot yet
A learning curve needs at least two training-set sizes, each scored on every fold — one size is a score, not a curve, and a single number cannot say whether the gap between training and validation is closing.
status="error" — Try again is wired to a real onRetry and flips the card to ready
Gradient boosting · learning curve
Training and validation R² against training-set size, each with a ±1 SD band across folds
Zyeon UI