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Created February 6, 2025 07:52
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Google Machine Learning Crash Course: Linear regression > taxi fare model training exercise
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google_course_ML_concepts_linear_regression_taxi.ipynb
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output_running_code_for_experiment_3.txt
INFO: starting training experiment with features=['TRIP_MILES', 'TRIP_MINUTES'] and label=FARE
Epoch 1/20
<ipython-input-19-8892146dfcd1>:8: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 3884.8274 - root_mean_squared_error: 62.2386
Epoch 2/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 1510.9357 - root_mean_squared_error: 38.7442
Epoch 3/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 273.6273 - root_mean_squared_error: 16.3169
Epoch 4/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 24.8084 - root_mean_squared_error: 4.9753
Epoch 5/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 15.6657 - root_mean_squared_error: 3.9543
Epoch 6/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 12.2236 - root_mean_squared_error: 3.4927
Epoch 7/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 11.2817 - root_mean_squared_error: 3.3472
Epoch 8/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 12.3543 - root_mean_squared_error: 3.5082
Epoch 9/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 12.9035 - root_mean_squared_error: 3.5800
Epoch 10/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 11.3703 - root_mean_squared_error: 3.3635
Epoch 11/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 12.7864 - root_mean_squared_error: 3.5701
Epoch 12/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 12.7018 - root_mean_squared_error: 3.5578
Epoch 13/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 12.3804 - root_mean_squared_error: 3.5061
Epoch 14/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 11.4949 - root_mean_squared_error: 3.3811
Epoch 15/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 13.1091 - root_mean_squared_error: 3.6081
Epoch 16/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 11.4416 - root_mean_squared_error: 3.3735
Epoch 17/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 10.9564 - root_mean_squared_error: 3.3044
Epoch 18/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 12.4541 - root_mean_squared_error: 3.5195
Epoch 19/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 11.0418 - root_mean_squared_error: 3.3155
Epoch 20/20
634/634 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 12.2511 - root_mean_squared_error: 3.4926
SUCCESS: training experiment complete
--------------------------------------------------------------------------------
| MODEL INFO |
--------------------------------------------------------------------------------
Weight for feature[TRIP_MILES]: 2.030
Weight for feature[TRIP_MINUTES]: 0.142
Bias: 3.828
FARE = 2.030 * TRIP_MILES + 0.142 * TRIP_MINUTES + 3.828
Raw
output_running_code_for_make_predictions.txt
--------------------------------------------------------------------------------
| PREDICTIONS |
--------------------------------------------------------------------------------
PREDICTED_FARE OBSERVED_FARE L1_LOSS TRIP_MILES TRIP_MINUTES
0 $18.33 $18.25 $0.08 4.90 32.00
1 $45.11 $43.25 $1.86 17.37 42.30
2 $31.50 $31.75 $0.25 11.98 23.52
3 $27.43 $27.00 $0.43 9.77 26.47
4 $74.23 $76.75 $2.52 31.06 51.58
5 $7.25 $9.00 $1.75 1.26 6.03
6 $5.96 $5.75 $0.21 0.70 5.00
7 $6.95 $9.00 $2.05 1.00 7.65
8 $33.96 $34.00 $0.04 12.60 32.00
9 $27.32 $26.00 $1.32 9.21 33.63
10 $44.20 $43.25 $0.95 17.54 33.48
11 $45.33 $45.00 $0.33 18.20 32.00
12 $17.74 $17.25 $0.49 5.20 23.58
13 $30.70 $30.50 $0.20 11.20 29.00
14 $43.36 $43.50 $0.14 17.30 31.00
15 $19.37 $19.25 $0.12 6.81 12.03
16 $17.82 $17.25 $0.57 5.70 17.00
17 $16.05 $15.75 $0.30 4.90 16.00
18 $9.01 $8.25 $0.76 2.20 5.00
19 $6.10 $5.75 $0.35 0.79 4.68
20 $17.17 $17.50 $0.33 5.10 21.00
21 $6.34 $5.75 $0.59 0.85 5.53
22 $41.13 $43.80 $2.67 14.94 48.95
23 $40.63 $41.25 $0.62 16.74 19.78
24 $46.86 $46.50 $0.36 17.90 47.00
25 $7.06 $6.75 $0.31 1.10 7.00
26 $21.07 $20.50 $0.57 7.30 17.00
27 $7.20 $6.75 $0.45 1.13 7.57
28 $19.21 $19.50 $0.29 6.13 20.65
29 $41.56 $40.50 $1.06 15.50 44.00
30 $31.48 $31.75 $0.27 11.62 28.50
31 $24.60 $24.25 $0.35 8.90 19.00
32 $17.99 $18.00 $0.01 5.66 18.78
33 $22.49 $22.50 $0.01 7.70 21.27
34 $22.16 $21.00 $1.16 6.44 36.92
35 $9.50 $9.00 $0.50 2.09 10.05
36 $24.32 $24.50 $0.18 8.50 22.70
37 $30.02 $30.25 $0.23 11.50 20.00
38 $43.81 $45.00 $1.19 17.80 27.02
39 $9.08 $9.00 $0.08 1.80 11.23
40 $5.62 $5.25 $0.37 0.60 4.00
41 $6.17 $5.50 $0.67 0.88 3.92
42 $14.91 $14.50 $0.41 4.22 17.68
43 $6.36 $5.25 $1.11 0.84 5.82
44 $15.60 $14.93 $0.67 4.74 15.12
45 $28.72 $36.00 $7.28 8.06 59.92
46 $21.79 $20.50 $1.29 7.26 22.63
47 $12.50 $12.00 $0.50 3.50 11.00
48 $9.39 $9.00 $0.39 2.27 6.67
49 $16.36 $15.75 $0.61 5.40 11.00
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