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import pandas as pd | |
from sklearn.ensemble import RandomForestRegressor | |
n = 100 # use every 100th row | |
df = pd.read_csv('{PATH_TO_DATA}train.csv', skiprows=lambda i: i % n != 0) | |
m = RandomForestRegressor() # instantiate the RandomForestRegressor objects | |
m.fit(X_train, y_train) # train the model | |
m.score(X_valid, y_valid) # score it on your validation set |
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import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn import metrics | |
preds = np.stack([t.predict(X_valid) for t in m.estimators_]) | |
preds[:,0], np.mean(preds[:,0]) | |
plt.plot([metrics.r2_score(y_valid, np.mean(preds[:i+1], axis=0)) for i in range(10)]); |
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m = RandomForestRegressor(n_jobs=-1) | |
m.fit(X_train, y_train) | |
print_score(m) |
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m = RandomForestRegressor(max_features='log2', n_jobs=-1) | |
m.fit(X_train, y_train) | |
print_score(m) |
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m = RandomForestRegressor(max_features='sqrt', n_jobs=-1) | |
m.fit(X_train, y_train) | |
print_score(m) |
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m = RandomForestRegressor(n_estimators=100, max_features='log2', n_jobs=-1) | |
m.fit(X_train, y_train) | |
print_score(m) |
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from starlette.applications import Starlette | |
from starlette.responses import HTMLResponse, JSONResponse | |
from starlette.staticfiles import StaticFiles | |
from starlette.middleware.cors import CORSMiddleware | |
import uvicorn, aiohttp, asyncio | |
from io import BytesIO | |
from fastai import * | |
from fastai.vision import * |