calibration-curve · pygal · anyplot.ai000.10.10.20.20.30.30.40.40.50.50.60.60.70.70.80.80.90.9110.00.00.10.10.20.20.30.30.40.40.50.50.60.60.70.70.80.80.90.91.01.0calibration-curve · pygal · anyplot.aiMean Predicted ProbabilityFraction of PositivesPerfect calibration reference0.0: 088.699999999999992230.269230769231Predicted = Observed0.2: 0.251197.44999999999981683.6346153846152Ideal: 50% predicted → 50% positive0.5: 0.52306.21137.0Predicted = Observed0.8: 0.753414.95590.3653846153848Perfect calibration reference1.0: 14523.743.73076923076951Curve start0.0: 088.699999999999992230.269230769231Bin: 0.05 pred → 0.14 actual (14% positive)0.0: 0.1444444444299.433247352083641914.4358974358975Bin: 0.16 pred → 0.20 actual (20% positive)0.2: 0.2015503876788.11124701057381789.5715563506262Bin: 0.25 pred → 0.30 actual (30% positive)0.3: 0.3030303031198.03350334353011567.681818181818Bin: 0.35 pred → 0.31 actual (30% positive)0.4: 0.30566037741643.2084405259641561.9310595065313Bin: 0.45 pred → 0.48 actual (47% positive)0.4: 0.47931034482073.2699464241331182.238726790451Bin: 0.55 pred → 0.52 actual (52% positive)0.6: 0.52398523992536.62061995570141084.5553505535054Bin: 0.65 pred → 0.64 actual (63% positive)0.6: 0.63888888892957.467309891077833.3141025641028Bin: 0.75 pred → 0.74 actual (74% positive)0.7: 0.74468085113403.92267155783601.9959083469723Bin: 0.84 pred → 0.77 actual (76% positive)0.8: 0.76821192053826.409625069252550.544319918492Bin: 0.95 pred → 0.89 actual (89% positive)1.0: 0.89156626514303.8258933610805280.8253012048192Curve end1.0: 14523.743.73076923076951Curve start0.0: 088.699999999999992230.269230769231Bin: 0.04 pred → 0.22 actual (Δ=-0.18)0.0: 0.2193995381276.846371100492151750.5437022561734Bin: 0.15 pred → 0.31 actual (Δ=-0.16)0.1: 0.3085106383739.38124380026141555.6988543371522Bin: 0.25 pred → 0.41 actual (Δ=-0.16)0.2: 0.41176470591186.23336875941071329.9298642533936Bin: 0.35 pred → 0.41 actual (Δ=-0.07)0.3: 0.41346153851630.3899333737461326.219674556213Bin: 0.45 pred → 0.40 actual (Δ=+0.05)0.5: 0.4040404042100.1902997994591346.8193473193473Bin: 0.55 pred → 0.59 actual (Δ=-0.04)0.6: 0.59139784952546.666830282242937.1550868486352Bin: 0.65 pred → 0.54 actual (Δ=+0.11)0.7: 0.54166666672979.2250807515351045.8942307692307Bin: 0.75 pred → 0.59 actual (Δ=+0.17)0.8: 0.58518518523416.449473095404950.7393162393164Bin: 0.86 pred → 0.63 actual (Δ=+0.23)0.9: 0.62735849063888.0811550748385858.5257619738752Bin: 0.96 pred → 0.80 actual (Δ=+0.15)1.0: 0.80416666674331.827809912818471.9278846153845Curve end1.0: 14523.743.73076923076951Perfect CalibrationLogistic Regression (Brier: 0.209)Overconfident Model (Brier: 0.227)