Datasets:

colinconwell
/

ml4hl

child_id int64	site int64	intervention int64	sex large_string	birthweight_g float64	gestational_age_wk float64	head_circumference_cm float64	neonatal_health float64	apgar5 float64	prenatal_care_trimester int64	prenatal_cigs_per_day float64	maternal_age float64	maternal_ed_level int64	maternal_vocab_ppvt float64	perinatal_stress_index float64	vocab_36m float64	vocab_60m float64	vocab_96m float64	reading_96m float64	math_96m float64	iq_36m float64	iq_96m float64	internalizing_36m float64	externalizing_36m float64	internalizing_96m float64	externalizing_96m float64	behavior_problems_96m float64
1,029	1	1	female	1,559	30	null	94	9	1	0	33	4	75	3.45	111	98	93	111	90	120	110	0	4	3	1	4
1,030	1	1	female	1,420	36	27	85	8	0	0	15	1	73	3.32	null	74	77	113	100	90	87	7	10	9	18	29
1,046	1	0	male	1,000	32	25	89	3	1	20	33	1	77	6.23	79	39	68	71	68	76	73	7	23	14	38	55
1,050	1	0	male	1,430	34	29	112	9	1	0	22	1	62.372353	3.05	null	null	19	25	0	43	40	12	34	7	13	21
1,056	1	0	male	1,984	38	31	99	9	2	10	20	3	73	2.46	89	69	94	75	92	73	74	6	28	13	31	47
1,059	1	0	female	1,320	33	28	110	7	2	5	23	3	56	3.51	79	71	63	91	92	83	82	15	26	11	9	24
1,069	1	0	female	2,020	35	29	100	7	1	30	37	1	64	4.59	93	72	80	97	98	95	93	4	24	7	16	23
1,079	1	1	female	1,900	34	30	110	9	2	0	13	1	80	3.1	92	87	91	120	106	110	103	7	26	6	15	27
1,085	1	0	female	2,041	31	31	98	9	1	0	41	1	79	3.67	90	86	74	88	78	71	66	8	22	27	24	53
1,086	1	0	female	1,320	30	null	110	7	1	0	27	4	91	3.01	83	94	86	110	115	98	112	16	33	3	5	8
1,090	1	0	female	2,150	33	32	87	9	2	0	30	1	56	4.06	75	75	76	93	98	95	97	15	42	19	23	46
1,091	1	0	male	1,620	32	28	109	7	1	0	22	1	73	3.42	96	80	92	109	122	83	101	3	9	3	6	8
1,094	1	1	female	1,550	32	29	74	5	1	0	25	4	83	3.89	103	94	89	115	106	110	94	2	13	1	1	2
1,096	1	0	male	1,814	33	30	98	8	1	20	35	3	106	3.96	90	94	104	114	96	95	106	6	30	12	28	46
1,102	1	0	male	1,280	30	28	106	7	2	0	26	2	72	4.46	null	51	78	64	80	66	81	15	31	12	22	35
1,103	1	0	female	1,210	30	26	100	6	1	0	18	2	70	4.33	75	81	78	80	87	83	95	9	26	2	4	7
1,105	1	0	female	2,296	33	null	99	9	1	0	25	1	95	2.9	102	94	103	107	104	100	108	9	12	15	11	30
1,107	1	1	male	810	26	24	92	5	1	0	27	2	84	6.01	null	29	43	51	20	55	44	13	33	15	24	41
1,112	1	1	female	2,270	35	32	104	9	2	10	19	1	61	3.36	81	39	65	73	90	81	80	9	17	5	4	12
1,113	1	0	male	1,670	33	30	104	4	2	20	18	1	59	5.09	72	47	38	46	32	62	44	14	34	4	15	20
1,114	1	0	female	2,030	35	31	131	9	2	0	18	1	66	1.81	69	47	68	92	100	73	90	15	46	11	25	37
1,122	1	0	female	2,030	35	32	125	9	2	20	23	3	84.838679	2.57	58	43	66	72	59	73	73	20	56	15	41	58
1,128	1	0	female	1,840	32	29	81	null	1	2	18	2	83	4.15	69	null	38	35	22	64	47	6	18	16	22	41
1,133	1	1	male	1,550	36	28	121	7	1	6	19	2	56	2.07	94	88	93	92	94	95	89	9	32	9	14	24
1,137	1	1	female	2,330	31	32	89	7	2	20	26	1	79	5.48	104	94	84	99	94	108	101	1	16	10	32	44
1,138	1	0	male	1,310	31	28	102	8	1	0	28	3	78	3.53	96	97	101	120	98	105	112	1	11	13	29	44
1,139	1	0	male	2,350	34	33	93	9	1	0	23	2	78	2.5	85	78	86	93	113	90	97	3	7	7	17	29
1,151	1	0	female	1,990	36	31	103	9	1	0	25	3	58	1.62	75	93	91	116	117	93	105	15	26	5	0	5
1,159	1	0	male	1,400	32	28	109	8	2	0	34	2	65	3.75	63	39	50	71	81	66	62	11	21	9	23	36
1,161	1	0	female	1,950	34	30	101	8	1	0	29	2	50	2.58	null	85	91	100	94	93	95	20	35	18	34	54
1,163	1	0	female	1,990	34	30	101	9	1	0	26	3	85	2.16	104	93	91	112	108	110	88	6	18	4	10	14
1,166	1	1	male	1,776	34	28	83	6	1	0	23	2	80	3.56	107	105	114	109	100	110	99	2	14	4	5	9
1,170	1	0	male	1,750	32	33	58	8	1	0	28	3	110	4.76	106	113	103	92	114	115	95	7	25	18	13	34
1,171	1	1	female	2,140	35	34	119	9	2	8	28	2	83	2.34	79	45	55	97	99	78	89	8	23	7	19	33
1,179	1	1	female	1,150	29	25	93	7	1	0	27	2	75	4.92	81	81	94	129	136	90	100	13	20	6	10	19
1,186	1	0	male	2,369	37	35	92	7	1	0	32	3	86	1.47	114	84	75	105	114	115	125	4	24	9	11	22
1,196	1	0	male	2,250	34	30	113	9	2	0	15	1	69	2.58	96	75	57	96	90	81	89	7	24	13	33	48
1,199	1	0	female	2,210	34	31	85	9	1	0	33	2	87	3	85	88	83	109	101	93	87	28	42	22	35	60
1,202	1	0	female	1,770	35	31	128	9	2	10	29	2	50	2.47	77	56	70	61	67	78	69	22	39	35	50	90
1,207	1	0	female	730	28	22	98	8	1	0	22	3	89	5.01	89	85	89	112	103	88	92	7	20	2	12	15
1,212	1	0	female	1,510	30	28	100	7	1	0	26	3	92	3.62	104	90	93	105	125	115	108	9	16	13	13	30
1,214	1	0	male	2,360	37	33	103	9	2	0	20	2	63	1.85	75	57	77	95	125	88	84	11	28	7	6	17
1,218	1	0	female	2,500	37	30	88	9	1	0	28	1	69	2.26	92	86	86	115	96	73	87	6	18	3	9	14
1,219	1	0	female	1,880	36	30	109	6	1	0	34	3	80	1.48	72	80	98	105	131	78	99	6	4	12	6	20
1,221	1	0	male	2,220	33	32	87	9	2	0	23	2	60	3.6	77	47	44	39	20	76	50	28	56	14	29	47
1,223	1	0	female	2,350	34	28	99	9	2	0	21	2	74	2.71	83	82	77	99	121	81	95	11	22	4	8	15
1,224	1	1	male	1,370	32	28	116	8	2	0	24	3	62	3.1	81	69	30	59	29	68	48	7	7	15	18	38
1,226	1	1	female	910	28	25	84	5	1	20	22	2	85	7.06	104	66	74	100	101	113	87	4	23	2	25	33
1,230	1	1	male	2,445	35	32	98	9	2	20	32	1	86	4.06	101	82	83	97	90	90	86	10	37	16	31	49
1,231	1	1	male	1,550	33	28	100	6	2	0	23	2	79	3.76	83	72	50	85	86	83	69	6	23	15	22	36
1,232	1	1	female	2,180	34	31	87	8	2	0	29	1	84	3.8	108	null	125	113	127	105	113	19	44	11	30	44
1,235	1	1	female	2,070	30	30	75	8	2	0	18	1	53.702041	5.34	93	74	51	93	77	93	83	9	18	11	24	38
1,236	1	1	male	1,786	32	31	42	8	1	0	25	4	102	5.04	107	88	94	102	103	115	88	3	16	3	13	14
1,238	1	0	female	2,160	34	32	118	9	1	0	25	2	103	1.61	85	72	120	113	90	83	77	8	24	11	23	34
1,242	1	0	female	1,928	32	30	89	9	1	0	34	3	90	3.2	83	113	106	122	134	98	113	1	4	8	4	13
1,244	1	0	female	1,750	35	29	99	9	1	0	17	1	46	3.05	69	63	38	88	80	62	69	9	26	15	42	61
1,251	1	1	female	2,350	34	32	113	9	1	20	18	1	68	3.25	81	103	109	96	96	105	100	9	27	17	24	38
1,255	1	0	female	1,890	31	31	81	7	1	0	20	2	79	4.2	81	76	91	93	92	86	94	6	14	12	23	34
1,260	1	0	male	2,410	34	31	65	9	1	0	40	2	102	3.66	null	26	19	45	14	48	44	12	28	14	36	60
1,261	1	1	female	2,500	34	33	91	9	2	0	25	1	72	3.28	90	90	89	122	125	120	106	8	11	2	2	8
1,264	1	0	male	2,353	34	31	99	9	1	10	28	3	74	2.48	83	80	95	105	131	93	106	4	34	2	7	12
1,279	1	1	female	2,180	34	31	106	7	2	0	23	1	70	2.97	89	70	73	100	82	95	75	2	15	3	8	12
1,281	1	0	male	920	27	25	84	4	1	4	19	3	64.051206	5.89	69	82	83	88	83	78	78	1	14	8	20	27
1,299	1	1	female	1,985	35	29	108	9	1	15	24	3	81	2.57	110	93	100	120	124	108	104	1	26	4	6	14
1,302	1	1	male	1,516	30	29	98	7	1	0	26	3	69	3.7	81	69	94	116	111	86	90	15	32	21	31	57
1,308	1	0	female	1,970	34	30	117	8	1	0	19	2	63	1.86	79	86	70	97	100	90	87	12	29	15	36	55
1,311	1	1	male	1,650	32	30	92	1	2	0	20	2	62	4.23	90	74	53	110	122	86	77	0	6	17	8	30
1,317	1	0	female	2,390	35	30	105	9	2	0	19	1	49	2.56	69	56	71	75	84	83	75	1	4	0	3	4
1,318	1	0	female	1,750	34	30	119	9	2	0	16	1	87	2.86	69	88	92	125	114	84	98	2	25	18	39	56
1,320	1	1	male	1,970	32	31	107	9	1	0	33	4	107	1.98	107	100	114	128	138	110	121	9	30	3	11	15
1,329	1	0	female	1,400	31	28	107	9	2	0	18	1	60	4.44	81	47	58	74	81	76	82	17	24	14	34	47
1,335	1	0	female	2,155	35	32	110	9	1	0	28	3	96	1.36	90	86	102	105	121	98	95	11	23	4	7	13
1,338	1	1	male	2,300	37	32	116	9	2	10	20	1	88.785426	2.35	96	84	98	107	92	100	87	5	18	20	24	43
1,343	1	1	male	1,530	33	30	106	8	2	0	25	2	80	3.52	103	104	94	107	84	120	93	13	24	30	29	66
1,346	1	0	male	1,950	35	31	103	9	2	0	19	2	83	2.75	72	78	79	101	107	88	87	6	20	10	10	24
1,348	1	0	female	1,240	30	27	100	7	2	20	22	1	83	6.41	77	90	91	93	84	68	76	14	15	10	15	25
1,352	1	0	male	1,410	31	29	93	8	1	0	24	4	115	3.43	100	96	118	104	103	103	104	13	14	10	18	32
1,353	1	1	female	2,170	34	32	109	9	1	10	22	2	73	2.62	89	86	86	95	96	113	92	11	29	18	39	60
1,357	1	1	female	1,920	32	30	114	7	1	0	24	2	96	2.49	94	87	89	130	114	122	95	3	14	5	17	22
1,360	1	0	female	2,270	34	31	117	9	1	10	24	1	56.160329	2.54	97	93	100	105	120	100	94	12	36	10	36	54
1,361	1	0	male	1,840	30	30	72	8	1	0	24	2	92	4.87	89	100	95	105	125	108	103	5	13	5	7	13
1,362	1	1	female	1,670	34	30	113	8	1	0	23	3	115	1.98	109	105	120	140	114	113	110	9	27	8	12	24
1,367	1	1	female	2,230	33	33	100	8	1	10	23	2	86	3.17	102	85	101	92	101	113	86	9	33	17	38	52
1,372	1	1	male	2,140	34	34	102	6	1	0	19	1	82	2.71	69	57	60	75	62	78	56	11	27	17	32	56
1,376	1	0	male	1,520	31	29	99	9	2	0	20	2	72	4.28	89	102	85	96	86	88	81	8	18	26	37	61
1,380	1	0	female	1,070	30	26	85	6	2	20	19	1	80	7.25	93	null	112	105	117	88	95	6	23	3	6	10
1,386	1	1	male	2,025	37	32	106	7	1	0	19	1	66	1.99	87	66	86	74	103	83	77	13	13	2	7	11
1,388	1	1	female	1,350	34	29	116	8	1	0	21	3	70	2.2	100	101	85	118	104	103	97	6	29	3	19	26
1,391	1	1	male	2,200	33	34	108	7	1	0	27	4	43	1.46	94	100	89	115	131	110	116	9	18	28	38	72
1,394	1	0	male	1,790	32	29	73	8	1	0	26	4	113	3.68	102	101	87	117	98	108	97	5	13	14	9	28
1,395	1	0	male	2,220	34	33	67	6	2	25	29	2	95	5.83	58	29	46	51	39	52	62	9	35	17	32	51
1,397	1	0	female	890	26	24	89	7	1	20	22	3	104	6.93	115	120	110	113	101	127	100	10	19	31	25	62
1,400	1	0	male	1,420	30	35	86	2	1	20	18	1	95	6.35	69	65	73	98	106	67	89	10	26	14	22	39
1,401	1	0	male	1,730	31	29	90	7	1	0	24	3	99	3.6	98	105	118	120	114	103	103	10	30	10	21	32
1,402	1	1	male	1,560	33	27	107	7	2	10	17	1	50.17042	4.45	112	106	108	107	76	115	95	3	13	12	13	27
1,412	1	1	male	1,580	34	30	124	9	1	3	15	1	71.696147	2.54	98	92	109	105	96	115	104	5	29	16	27	47
1,414	1	1	male	1,956	32	31	99	10	3	0	22	3	71	3.68	94	96	94	99	125	113	123	10	32	9	26	38
1,417	1	0	female	2,325	37	32	98	9	1	20	19	2	82	2.89	103	106	99	115	135	103	114	3	22	12	12	28
1,420	1	0	male	1,580	30	29	87	9	1	0	23	2	93	4.5	111	93	98	96	96	95	106	4	15	6	11	17
1,421	1	0	male	2,296	33	32	72	9	1	3	35	4	107	3.13	94	108	130	116	131	108	118	4	20	2	12	18

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ML-4-HL: Machine Learning for Human Learners

Helper package and precomputed artifacts for the Machine Learning for Human Learners introductory tutorial. The tutorial notebook downloads these files automatically; you don't need to fetch anything by hand.

from huggingface_hub import snapshot_download
import sys

root = snapshot_download("colinconwell/ml4hl", repo_type="dataset", allow_patterns=["ml4hl/**"])
sys.path.insert(0, root)
import ml4hl
ml4hl.show("error_landscape")

ml4hl/: interactive widgets (ml4hl.show), simulators, and dataset loaders.
artifacts/: dollar_street_emb.npy, dollar_street_meta.parquet, dollar_street_thumbs.npy, finch_feats.npy, finch_feature_names.json, finch_meta.parquet, finch_specs.npy, household_survey.parquet, manifest.json, music_emotions.parquet, musiclab_ids.parquet, musiclab_nhs.parquet, perinatal_dev.parquet, things_meta.parquet, things_model_emb.npy, things_spose.npy, wordbank_cdi.parquet

Artifact	Rows	Built from
Dollar Street photo embeddings, thumbnails, incomes	1041	Gapminder Dollar Street (CC BY 4.0) via `iamshnoo/dollarstreet`
THINGS concepts: SPoSE human embedding + model text embedding	1854	THINGS initiative; Hebart et al. (2020)
Zebra finch call features and spectrograms	1619	Elie & Theunissen (2016) via `cgeorgiaw/animal-sounds`

Embeddings were computed with google/siglip2-base-patch16-224. Each artifact inherits the terms of its source dataset; please cite the original sources. The ml4hl code is MIT-licensed.

Author: Colin Conwell (conwell@mit.edu)

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ML-4-HL: Machine Learning for Human Learners

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