In this paper we measure parenting behavior through unsupervised machine learning in a panel following children from age 5 to 29 months. The algorithm classifies parents into two distinct behavioral types: "active" and "laissez-faire". Parents of the active type tend to respond to their children's expressions and describe to children features of their environment, while parents of the laissez-faire type are less likely to engage with their children. We find that parents' types are persistent over time and are systematically related to socio-economic characteristics. More-over, children of active parents see their human capital improve relative to children of parents of the laissez-faire type.